{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4656b2fb-c1a1-42e7-b104-a4dd17167a17",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\AppData\\Local\\Temp\\ipykernel_6952\\3136565085.py:30: FutureWarning: DataFrame.applymap has been deprecated. Use DataFrame.map instead.\n",
      "  df[condition_columns] = df[condition_columns].applymap(lambda x: 1 if str(x).strip().lower() in [\"yes\", \"1\", \"true\"] else 0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20, Loss: 69.714992\n",
      "Epoch 2/20, Loss: 21.676844\n",
      "Epoch 3/20, Loss: 13.904543\n",
      "Epoch 4/20, Loss: 12.514398\n",
      "Epoch 5/20, Loss: 11.214165\n",
      "Epoch 6/20, Loss: 10.463312\n",
      "Epoch 7/20, Loss: 9.612863\n",
      "Epoch 8/20, Loss: 9.463587\n",
      "Epoch 9/20, Loss: 9.059246\n",
      "Epoch 10/20, Loss: 9.068037\n",
      "Epoch 11/20, Loss: 9.089166\n",
      "Epoch 12/20, Loss: 8.865161\n",
      "Epoch 13/20, Loss: 8.977364\n",
      "Epoch 14/20, Loss: 9.134089\n",
      "Epoch 15/20, Loss: 9.171653\n",
      "Epoch 16/20, Loss: 9.248043\n",
      "Epoch 17/20, Loss: 9.309088\n",
      "Epoch 18/20, Loss: 9.673072\n",
      "Epoch 19/20, Loss: 9.512491\n",
      "Epoch 20/20, Loss: 9.350298\n",
      "Epoch 1/20, Loss: 29.530220\n",
      "Epoch 2/20, Loss: 10.355756\n",
      "Epoch 3/20, Loss: 9.423891\n",
      "Epoch 4/20, Loss: 9.059593\n",
      "Epoch 5/20, Loss: 8.718587\n",
      "Epoch 6/20, Loss: 9.050529\n",
      "Epoch 7/20, Loss: 8.844004\n",
      "Epoch 8/20, Loss: 8.457669\n",
      "Epoch 9/20, Loss: 8.382594\n",
      "Epoch 10/20, Loss: 7.628492\n",
      "Epoch 11/20, Loss: 7.542578\n",
      "Epoch 12/20, Loss: 7.451040\n",
      "Epoch 13/20, Loss: 7.396304\n",
      "Epoch 14/20, Loss: 7.740430\n",
      "Epoch 15/20, Loss: 6.918175\n",
      "Epoch 16/20, Loss: 6.886579\n",
      "Epoch 17/20, Loss: 6.326677\n",
      "Epoch 18/20, Loss: 6.120960\n",
      "Epoch 19/20, Loss: 5.792683\n",
      "Epoch 20/20, Loss: 5.851411\n"
     ]
    },
    {
     "data": {
      "image/png": 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WGGaIiIjIrdX7m+aZzWZcvXoVPj4+drv5FhERETmWEAKZmZkIDQ2FQlF520u9DzNXr14t86RXIiIicg+XLl1Co0aNKp2n3ocZHx8fAJYvw9fX18XVEBERUVVkZGQgPDxc/h2vTL0PM9ZTS76+vgwzREREbqYqXUTYAZiIiIjcGsMMERERuTWGGSIiInJr9b7PDBFRXWQymVBQUODqMohcxsPDA0ql0i7rYpghInIiIQSSk5ORlpbm6lKIXK5BgwYwGAy1vg8cwwwRkRNZg0xQUBC0Wi1v5kl3JCEEcnJykJKSAgAICQmp1foYZoiInMRkMslBJiAgwNXlELmUl5cXACAlJQVBQUG1OuXEDsBERE5i7SOj1WpdXAlR3WD9u1Db/mMuDTNNmzaFJEllhilTpgCwNEPFxsYiNDQUXl5eiI6OxvHjx11ZMhFRrfHUEpGFvf4uuDTMHDx4EElJSfIQFxcHAHj44YcBAAsXLsTixYuxdOlSHDx4EAaDATExMcjMzHRl2URERFSHuDTMBAYGwmAwyMOWLVvQvHlz9OnTB0IILFmyBLNnz8bIkSPRvn17rF69Gjk5OVi3bp0ryyYiojoqOjoa06ZNc3UZdnX+/HlIkoQjR444fFuSJGHTpk0O34691Zk+M/n5+Vi7di2eeOIJSJKExMREJCcnY8CAAfI8Go0Gffr0QXx8fIXrMRqNyMjIsBmIiKj24uPjoVQqMWjQoDLTYmNjcdddd5UZ76gfx927d0OSpDKXuG/YsAFvvPGG3bdXkjVcqFQqXLlyxWZaUlISVCoVJEnC+fPnHVqHIyQlJWHw4MGuLqPa6kyY2bRpE9LS0jBp0iQAlssXASA4ONhmvuDgYHlaeebPnw+9Xi8P4eHhDqnXWGjClbRcXMvIc8j6iYjqmhUrVuDZZ5/FTz/9hIsXL7q6nHL5+/tX6SnL9hAaGoo1a9bYjFu9ejXCwsKcsn1HMBgM0Gg0ri6j2upMmFm+fDkGDx6M0NBQm/GlOwcJISrtMDRr1iykp6fLw6VLlxxS7792/Il7FuzEv3f96ZD1ExHVJdnZ2fjiiy/wzDPPYOjQoVi1apU8bdWqVZg7dy5+//13+UKOVatWoWnTpgCABx98EJIkyZ8B4JtvvkHXrl3h6emJZs2aYe7cuSgsLJSnS5KETz75BA8++CC0Wi0iIyOxefNmAJaWkb59+wIA/Pz8IEmS/B/h0qeZUlNT8dhjj8HPzw9arRaDBw/GmTNnbGpv0KABfvjhB7Rp0wbe3t4YNGgQkpKSbvudTJw4EStXrrQZt2rVKkycOLHMvCdOnMCQIUPg7e2N4OBgTJgwATdu3JCnm81mvP3222jRogU0Gg0aN26Mt956y2Yd586dQ9++faHVatGpUyfs27dPnnbz5k2MHTsWjRo1glarRYcOHfDZZ5/ZLB8dHY3nnnsO06dPh7+/PwwGA2JjY23mKdmSlp+fj6lTpyIkJASenp5o2rQp5s+fbzPvf//7XwwdOhRarRZt2rTBvn378OeffyI6Oho6nQ5RUVE4e/bsbb/LWhN1wPnz54VCoRCbNm2Sx509e1YAEIcOHbKZ94EHHhCPPfZYldednp4uAIj09HS71SuEEKt+ThRNZmwRT//vV7uul4jqr9zcXHHixAmRm5srhBDCbDaLbGOBSwaz2Vyt2pcvXy66desmhBDim2++EU2bNpXXkZOTI1566SXRrl07kZSUJJKSkkROTo5ISUkRAMTKlStFUlKSSElJEUIIsXXrVuHr6ytWrVolzp49K7Zt2yaaNm0qYmNj5e0BEI0aNRLr1q0TZ86cEc8995zw9vYWN2/eFIWFheLrr78WAERCQoJISkoSaWlpQggh+vTpI55//nl5PQ888IBo06aN+PHHH8WRI0fEwIEDRYsWLUR+fr4QQoiVK1cKDw8P0b9/f3Hw4EHx22+/iTZt2ohx48ZV+F0kJiYKAOLAgQOiYcOGYu/evUIIIfbu3SsCAwPFgQMHBACRmJgohBDi6tWromHDhmLWrFni5MmT4tChQyImJkb07dtXXuf06dOFn5+fWLVqlfjzzz/F3r17xccff2yzvdatW4stW7aIhIQE8dBDD4kmTZqIgoICIYQQly9fFu+88444fPiwOHv2rPjggw+EUqkU+/fvl7fRp08f4evrK2JjY8Xp06fF6tWrhSRJYtu2bTbf+8aNG4UQQrzzzjsiPDxc/Pjjj+L8+fNi7969Yt26dTbzhoWFic8//1wkJCSIESNGiKZNm4r77rtPbN26VZw4cUL07NlTDBo0qMLvsvTfiZKq8/tdJ26at3LlSgQFBeH++++Xx0VERMBgMCAuLg6dO3cGYEmJe/bswdtvv+2qUmWBPpZmuOuZRhdXQkTuKrfAhLav/eCSbZ94fSC06qr/BCxfvhzjx48HAAwaNAhZWVnYsWMH+vfvDy8vL3h7e0OlUsFgMMjLWG+KZr1lvdVbb72FmTNnyi0YzZo1wxtvvIHp06djzpw58nyTJk3C2LFjAQDz5s3Dv/71Lxw4cACDBg2Cv78/ACAoKAgNGjQot+YzZ85g8+bN+Pnnn9GrVy8AwKefforw8HBs2rRJvnK2oKAA//nPf9C8eXMAwNSpU/H666/f9jvx8PDA+PHjsWLFCvTu3RsrVqzA+PHj4eHhYTPfsmXL0KVLF8ybN08et2LFCoSHh+P06dMICQnB+++/j6VLl8rfSfPmzdG7d2+b9bz88svy7+TcuXPRrl07/Pnnn2jdujXCwsLw8ssvy/M+++yz2Lp1K7788kv06NFDHt+xY0f5O46MjMTSpUuxY8cOxMTElNm/ixcvIjIyEr1794YkSWjSpEmZeR5//HGMHj0aADBjxgxERUXh1VdfxcCBAwEAzz//PB5//PHbfpe15fIwYzabsXLlSkycOBEqVXE5kiRh2rRpmDdvHiIjIxEZGYl58+ZBq9Vi3LhxLqzYQg4zWQwzRFS/JSQk4MCBA9iwYQMAQKVSYcyYMVixYgX69+9f7fX99ttvOHjwoM1pFJPJhLy8POTk5Mg3UuvYsaM8XafTwcfHR779fVWcPHkSKpXK5sc8ICAArVq1wsmTJ+VxWq1WDjKA5db6Vd3Ok08+iaioKMybNw9ffvkl9u3bZ3O6zLq/u3btgre3d5nlz549i7S0NBiNRvTr16/SbZX8Pqy3/09JSUHr1q1hMpmwYMECfP7557hy5QqMRiOMRiN0Ol2F67jdvk6aNAkxMTFo1aoVBg0ahKFDh9pclFN6fdY+rh06dLAZl5eXh4yMDPj6+la6f7Xh8jCzfft2XLx4EU888USZadOnT0dubi4mT56M1NRU9OjRA9u2bXNa567KBHqzZYaIasfLQ4kTrw902baravny5SgsLLTp2CqEgIeHB1JTU+Hn51etbZvNZsydOxcjR44sM83T01N+X7qFQ5IkmM3mKm/Hciak/PEl+16Wt52Kli2tffv2aN26NcaOHYs2bdqgffv2ZS6hNpvNGDZsWLlnFUJCQnDu3Lkqbatkndb6rd/HokWL8N5772HJkiXo0KEDdDodpk2bhvz8/ArXYV1PRd9ply5dkJiYiO+//x7bt2/H6NGj0b9/f3z11VeV1lRZnY7i8jAzYMCACv/QSJKE2NjYMh2U6gJry0xOvgnZxkLoNC7/KonIzUiSVK1TPa5QWFiINWvWYNGiRWX+Vz5q1Ch8+umnmDp1KtRqNUwmU5nlPTw8yozv0qULEhIS0KJFixrXpVarAaDcbVq1bdsWhYWF+OWXX+TTTDdv3sTp06fRpk2bGm+7tCeeeAKTJ0/GsmXLyp3epUsXfP3112jatKnNGQiryMhIeHl5YceOHfjrX/9aoxr27t2L4cOHy6cCzWYzzpw5U+v99PX1xZgxYzBmzBg89NBDGDRoEG7duiWf5qsr6szVTO5Gp1FBq7b8z4atM0RUX23ZsgWpqal48skn0b59e5vhoYcewvLlywFYHk+TmJiII0eO4MaNGzAajfL4HTt2IDk5GampqQCA1157DWvWrEFsbCyOHz+OkydP4vPPP8c///nPKtfVpEkTSJKELVu24Pr168jKyiozT2RkJIYPH46//e1v+Omnn/D7779j/PjxCAsLw/Dhw+3w7Vj87W9/w/Xr1ysMIlOmTMGtW7cwduxYHDhwAOfOncO2bdvwxBNPwGQywdPTEzNmzMD06dOxZs0anD17Fvv375e/26po0aIF4uLiEB8fj5MnT+Kpp56q9DYmVfHee+9h/fr1OHXqFE6fPo0vv/wSBoOhwj5KrsQwUwvsN0NE9d3y5cvRv39/6PX6MtNGjRqFI0eO4NChQxg1ahQGDRqEvn37IjAwUL4seNGiRYiLi0N4eLh8McfAgQOxZcsWxMXFoXv37ujZsycWL15cbgfTioSFhWHu3LmYOXMmgoODMXXq1HLnW7lyJbp27YqhQ4ciKioKQgh89913ZU631IZKpULDhg3LbXUBLPej+fnnn2EymTBw4EC0b98ezz//PPR6PRQKy8/wq6++ipdeegmvvfYa2rRpgzFjxlSrf9Crr76KLl26YODAgYiOjobBYMCIESNqtV/e3t54++230a1bN3Tv3h3nz5/Hd999J9dcl0iiqicG3VRGRgb0ej3S09Pt3vnooWXx+PVCKj58tAuGdAix67qJqP7Jy8tDYmIiIiIibPqGEN2pKvs7UZ3f77oXr9wIL88mIiJyPYaZWghimCEiInI5hplaYMsMERGR6zHM1AI7ABMREbkew0wtsGWGiIjI9RhmaiHQ29LzmmGGiIjIdRhmasHaMnMjywizuV5f4U5ERFRnMczUQoC35XbahWaB1Jz828xNREREjsAwUwseSgX8dZZAw07ARERErsEwU0t8ejYRkfPl5ORg1KhR8PX1hSRJSEtLc3VJTrFq1ao6+WwkV2OYqSVe0URE9ZkkSZUOkyZNckldq1evxt69exEfH4+kpKRynx1VU+fPn4ckSThy5Ijd1mkvY8aMwenTp11dRp1Tt5897wYYZoioPktKSpLff/7553jttdeQkJAgj/Py8rKZv6CgwK4PcazI2bNn0aZNG7Rv377G6zCZTJAkqU48ODE/Px9qtfq283l5eZX5zoktM7XGMENE9ZnBYJAHvV4PSZLkz3l5eWjQoAG++OILREdHw9PTE2vXrsXNmzcxduxYNGrUCFqtFh06dJCfom0VHR2N5557DtOnT4e/vz8MBgNiY2Nt5omNjUXjxo2h0WgQGhqK5557Tl520aJF+PHHHyFJEqKjowFYAsH06dMRFhYGnU6HHj16YPfu3fL6rKdotmzZgrZt20Kj0eDChQvV/k6EEFi4cCGaNWsGLy8vdOrUCV999ZU83WQy4cknn0RERAS8vLzQqlUrvP/++zbrmDRpEkaMGIH58+cjNDQULVu2lFuENmzYgL59+0Kr1aJTp07Yt29fmX0o+R3ddddd+N///oemTZtCr9fjkUceQWZmpjxPZmYmHn30Ueh0OoSEhOC9995DdHQ0pk2bVu19r6vYMlNLcp8ZdgAmouoSAijIcc22PbSAJNllVTNmzMCiRYuwcuVKaDQa5OXloWvXrpgxYwZ8fX3x7bffYsKECWjWrBl69OghL7d69Wq8+OKL+OWXX7Bv3z5MmjQJ99xzD2JiYvDVV1/hvffew/r169GuXTskJyfj999/BwBs2LABM2fOxB9//IENGzbILRqPP/44zp8/j/Xr1yM0NBQbN27EoEGDcOzYMURGRgKw9LWZP38+PvnkEwQEBCAoKKja+/vPf/4TGzZswLJlyxAZGYkff/wR48ePR2BgIPr06QOz2YxGjRrhiy++QMOGDREfH4+///3vCAkJwejRo+X17NixA76+voiLi4MQxbf3mD17Nt59911ERkZi9uzZGDt2LP7880+oVOX/ZJ89exabNm3Cli1bkJqaitGjR2PBggV46623AAAvvvgifv75Z2zevBnBwcF47bXXcOjQIdx1113V3ve6imGmltgyQ0Q1VpADzAt1zbZfuQqodXZZ1bRp0zBy5EibcS+//LL8/tlnn8XWrVvx5Zdf2oSZjh07Ys6cOQCAyMhILF26FDt27EBMTAwuXrwIg8GA/v37w8PDA40bN8bdd98NAPD394dWq4VarYbBYABg+UH/7LPPcPnyZYSGhso1bN26FStXrsS8efMAWE6Dffjhh+jUqVON9jU7OxuLFy/Gzp07ERUVBQBo1qwZfvrpJ/z3v/9Fnz594OHhgblz58rLREREID4+Hl988YVNmNHpdPjkk0/kMHb+/Hm57vvvvx8AMHfuXLRr1w5//vknWrduXW5NZrMZq1atgo+PDwBgwoQJ2LFjB9566y1kZmZi9erVWLduHfr16wcAWLlypfwd1RcMM7XEMENEd7pu3brZfDaZTFiwYAE+//xzXLlyBUajEUajETqdbXjq2LGjzeeQkBCkpKQAAB5++GEsWbIEzZo1w6BBgzBkyBAMGzaswtaJQ4cOQQiBli1b2ow3Go0ICAiQP6vV6jLbrY4TJ04gLy8PMTExNuPz8/PRuXNn+fN//vMffPLJJ7hw4QJyc3ORn59fpiWkQ4cO5faTKVlfSEgIACAlJaXCMNO0aVM5yFiXsX6P586dQ0FBgRwEAUCv16NVq1ZV3GP3wDBTS3zYJBHVmIfW0kLiqm3bSemQsmjRIrz33ntYsmQJOnToAJ1Oh2nTpiE/3/bmoqU7CkuSBLPZDAAIDw9HQkIC4uLisH37dkyePBnvvPMO9uzZU24HY7PZDKVSid9++w1KpdJmmre3t/zey8sLUi1Or1nr+/bbbxEWFmYzTaOx/B588cUXeOGFF7Bo0SJERUXBx8cH77zzDn755Reb+Ut/b1Yl989aq3W7t5vfuox1fuvpq9L7XPK0Vn3AMFNLQUVhJi2nAMZCEzQq5W2WICIqIkl2O9VTl+zduxfDhw/H+PHjAVh+iM+cOYM2bdpUaz1eXl544IEH8MADD2DKlClo3bo1jh07hi5dupSZt3PnzjCZTEhJScG9995rl/0oj7Xj8MWLF9GnT59y59m7dy969eqFyZMny+POnj3rsJoq07x5c3h4eODAgQMIDw8HAGRkZODMmTMV1u+OGGZqSe/lAQ+lhAKTwM2sfIQ24CVzRHRna9GiBb7++mvEx8fDz88PixcvRnJycrXCzKpVq2AymdCjRw9otVr873//g5eXF5o0aVLu/C1btsSjjz6Kxx57DIsWLULnzp1x48YN7Ny5Ex06dMCQIUOqvR8lL0G3atu2LV5++WW88MILMJvN6N27NzIyMhAfHw9vb29MnDgRLVq0wJo1a/DDDz8gIiIC//vf/3Dw4EFERERUu4ba8vHxwcSJE/GPf/wD/v7+CAoKwpw5c6BQKGrVQlXXMMzUkiRJCPTW4Gp6Hq5nGhlmiOiO9+qrryIxMREDBw6EVqvF3//+d4wYMQLp6elVXkeDBg2wYMECvPjiizCZTOjQoQO++eYbm/4vpa1cuRJvvvkmXnrpJVy5cgUBAQGIioqqUZABgEceeaTMuMTERLzxxhsICgrC/Pnzce7cOTRo0ABdunTBK6+8AgB4+umnceTIEYwZMwaSJGHs2LGYPHkyvv/++xrVUVuLFy/G008/jaFDh8LX1xfTp0/HpUuX4Onp6ZJ6HEES9e3EWSkZGRnQ6/VIT0+Hr6+vQ7YxfOlP+P1yOj55rBv6tw12yDaIyP3l5eUhMTERERER9eqHhNxLdnY2wsLCsGjRIjz55JMuraWyvxPV+f1my4wdsBMwERHVVYcPH8apU6dw9913Iz09Ha+//joAYPjw4S6uzH4YZuzAGmZSMhhmiIio7nn33XeRkJAAtVqNrl27Yu/evWjYsKGry7Ibhhk7KL4LcJ6LKyEiIrLVuXNn/Pbbb64uw6H4bCY74I3ziIiIXIdhxg4YZoioOur5dRdEVWavvwsMM3bADsBEVBXWO7Xm5Ljo4ZJEdYz170J5d3WuDvaZsYNAb8vlZNczjRBC1KsbERGR/SiVSjRo0EB+bo5Wq+W/F3RHEkIgJycHKSkpaNCgQZlHUFQXw4wdNPSxPCgsr8CMLGMhfDxrlzCJqP6yPuXZGmiI7mQNGjSQ/07UBsOMHWjVKnhrVMgyFuJ6ppFhhogqJEkSQkJCEBQUhIKCAleXQ+QyHh4etW6RsWKYsZNAH40cZpoFet9+ASK6oymVSrv9Q050p2MHYDspvtcMOwETERE5E8OMnfDybCIiItdgmLEThhkiIiLXYJixE4YZIiIi12CYsRPeOI+IiMg1GGbshC0zRERErsEwYyfy1UwMM0RERE7FMGMnQUUtMzez82Ey8yFyREREzuLyMHPlyhWMHz8eAQEB0Gq1uOuuu/Dbb7/J04UQiI2NRWhoKLy8vBAdHY3jx4+7sOLy+evUkCTAZBa4lZ3v6nKIiIjuGC4NM6mpqbjnnnvg4eGB77//HidOnMCiRYvQoEEDeZ6FCxdi8eLFWLp0KQ4ePAiDwYCYmBhkZma6rvByqJQKBOgsz2jiqSYiIiLncenjDN5++22Eh4dj5cqV8rimTZvK74UQWLJkCWbPno2RI0cCAFavXo3g4GCsW7cOTz31lLNLrlRDbw1uZOXziiYiIiIncmnLzObNm9GtWzc8/PDDCAoKQufOnfHxxx/L0xMTE5GcnIwBAwbI4zQaDfr06YP4+Phy12k0GpGRkWEzOAuvaCIiInI+l4aZc+fOYdmyZYiMjMQPP/yAp59+Gs899xzWrFkDAEhOTgYABAcH2ywXHBwsTytt/vz50Ov18hAeHu7YnSiBYYaIiMj5XBpmzGYzunTpgnnz5qFz58546qmn8Le//Q3Lli2zmU+SJJvPQogy46xmzZqF9PR0ebh06ZLD6i+NYYaIiMj5XBpmQkJC0LZtW5txbdq0wcWLFwEABoMBAMq0wqSkpJRprbHSaDTw9fW1GZyFT84mIiJyPpeGmXvuuQcJCQk2406fPo0mTZoAACIiImAwGBAXFydPz8/Px549e9CrVy+n1loVxS0zeS6uhIiI6M7h0quZXnjhBfTq1Qvz5s3D6NGjceDAAXz00Uf46KOPAFhOL02bNg3z5s1DZGQkIiMjMW/ePGi1WowbN86VpZeLp5mIiIicz6Vhpnv37ti4cSNmzZqF119/HREREViyZAkeffRReZ7p06cjNzcXkydPRmpqKnr06IFt27bBx8fHhZWXL4hhhoiIyOkkIUS9vvd+RkYG9Ho90tPTHd5/Jj23AJ3mbgMAnHpjEDw9lA7dHhERUX1Vnd9vlz/OoD7x9VRBrbJ8pTfYCZiIiMgpGGbsSJIkPj2biIjIyRhm7IydgImIiJyLYcbO5DDD00xEREROwTBjZ2yZISIici6GGTuz9plJYZghIiJyCoYZO2PLDBERkXMxzNgZwwwREZFzMczYGcMMERGRczHM2FnJJ2fX85srExER1QkMM3ZmbZnJLzQjI6/QxdUQERHVfwwzdubpoYSPp+X5nTzVRERE5HgMMw7AfjNERETOwzDjACX7zRAREZFjMcw4AFtmiIiInIdhxgGCfDwBMMwQERE5A8OMA7BlhoiIyHkYZhyAT84mIiJyHoYZB2DLDBERkfMwzDiAfDUTwwwREZHDMcw4gLVl5la2ESYzH2lARETkSAwzDuCvU0MhAWYB3GS/GSIiIodimHEApUJCQNGpphSeaiIiInIohhkH4V2AiYiInINhxkF4RRMREZFzMMw4CMMMERGRczDMOAjDDBERkXMwzDgI+8wQERE5B8OMg7BlhoiIyDkYZhzEGmZuMMwQERE5FMOMg7BlhoiIyDkYZhwkqCjMZBoLkZtvcnE1RERE9RfDjIN4a1Tw9LB8vTfYCZiIiMhhGGYcRJIk+VQTH2lARETkOAwzDiRfns0wQ0RE5DAMMw4kdwLmaSYiIiKHYZhxIF7RRERE5HgMMw4U6O0JgGGGiIjIkRhmHKi4ZSbPxZUQERHVXwwzDsTTTERERI7HMONADDNERESO59IwExsbC0mSbAaDwSBPF0IgNjYWoaGh8PLyQnR0NI4fP+7Ciqun5NVMQggXV0NERFQ/ubxlpl27dkhKSpKHY8eOydMWLlyIxYsXY+nSpTh48CAMBgNiYmKQmZnpwoqrrqG3GgBQYBJIzy1wcTVERET1k8vDjEqlgsFgkIfAwEAAllaZJUuWYPbs2Rg5ciTat2+P1atXIycnB+vWrXNx1VWjUSmh9/IAwFNNREREjuLyMHPmzBmEhoYiIiICjzzyCM6dOwcASExMRHJyMgYMGCDPq9Fo0KdPH8THx7uq3GpjvxkiIiLHcmmY6dGjB9asWYMffvgBH3/8MZKTk9GrVy/cvHkTycnJAIDg4GCbZYKDg+Vp5TEajcjIyLAZXEl+pAHvAkxEROQQKldufPDgwfL7Dh06ICoqCs2bN8fq1avRs2dPAJYHNpYkhCgzrqT58+dj7ty5jim4BtgyQ0RE5FguP81Ukk6nQ4cOHXDmzBn5qqbSrTApKSllWmtKmjVrFtLT0+Xh0qVLDq35doIYZoiIiByqToUZo9GIkydPIiQkBBERETAYDIiLi5On5+fnY8+ePejVq1eF69BoNPD19bUZXIktM0RERI7l0tNML7/8MoYNG4bGjRsjJSUFb775JjIyMjBx4kRIkoRp06Zh3rx5iIyMRGRkJObNmwetVotx48a5suxq4ZOziYiIHMulYeby5csYO3Ysbty4gcDAQPTs2RP79+9HkyZNAADTp09Hbm4uJk+ejNTUVPTo0QPbtm2Dj4+PK8uuFrbMEBEROZYk6vmtaTMyMqDX65Genu6SU06nkjMwaMleBOjU+O3VGKdvn4iIyB1V5/e7TvWZqY+sl2bfyslHgcns4mqIiIjqH4YZB/PTqqFUSBACuJWd7+pyiIiI6h2GGQdTKCT5GU0pGew3Q0REZG8MM05QfEVTnosrISIiqn8YZpxAfqQBr2giIiKyO4YZJ+Dl2URERI7DMOMEDDNERESOwzDjBHxyNhERkeMwzDhBoI8nALbMEBEROQLDjBPwNBMREZHjMMw4AcMMERGR4zDMOEFQUZjJzjch21jo4mqIiIjqF4YZJ9BpVNCqlQCAG+wETEREZFcMM07CU01ERESOwTDjJLwLMBERkWMwzDhJ8fOZGGaIiIjsiWHGSXiaiYiIyDEYZpyEp5mIiIgcg2HGSdgyQ0RE5BgMM05iDTMpDDNERER2xTDjJGyZISIicgyGGSexhpkbWUaYzcLF1RAREdUfDDNOEqCzhJlCs0BaboGLqyEiIqo/GGacRK1SwE/rAYCnmoiIiOyJYcaJ2G+GiIjI/hhmnKj4LsB5Lq6EiIio/mCYcaIgH08AbJkhIiKyJ4YZJ+JpJiIiIvtjmHEiPtKAiIjI/hhmnIhPziYiIrI/hhkn4mkmIiIi+2OYcSKGGSIiIvtjmHEia5+Z1JwC5BeaXVwNERFR/cAw40R6Lw94KCUAwM1sts4QERHZA8OMEykUEhryiiYiIiK7YphxMmu/mZQMhhkiIiJ7YJhxMvleM7w8m4iIyC4YZpyMVzQRERHZF8OMkzHMEBER2RfDjJMxzBAREdkXw4yTsc8MERGRfTHMOBlbZoiIiOyrzoSZ+fPnQ5IkTJs2TR4nhEBsbCxCQ0Ph5eWF6OhoHD9+3HVF2kGQjycAS5gRQri4GiIiIvdXJ8LMwYMH8dFHH6Fjx4424xcuXIjFixdj6dKlOHjwIAwGA2JiYpCZmemiSmuvoY8aAJBbYEJ2vsnF1RAREbk/l4eZrKwsPProo/j444/h5+cnjxdCYMmSJZg9ezZGjhyJ9u3bY/Xq1cjJycG6detcWHHtaNUqeGtUAHiqiYiIyB5cHmamTJmC+++/H/3797cZn5iYiOTkZAwYMEAep9Fo0KdPH8THx1e4PqPRiIyMDJuhrmG/GSIiIvupUZi5dOkSLl++LH8+cOAApk2bho8++qha61m/fj0OHTqE+fPnl5mWnJwMAAgODrYZHxwcLE8rz/z586HX6+UhPDy8WjU5QyCfz0RERGQ3NQoz48aNw65duwBYQkdMTAwOHDiAV155Ba+//nqV1nHp0iU8//zzWLt2LTw9PSucT5Ikm89CiDLjSpo1axbS09Pl4dKlS1Wqx5mKW2byXFwJERGR+6tRmPnjjz9w9913AwC++OILtG/fHvHx8Vi3bh1WrVpVpXX89ttvSElJQdeuXaFSqaBSqbBnzx588MEHUKlUcotM6VaYlJSUMq01JWk0Gvj6+toMdY0cZnivGSIiolqrUZgpKCiARmP5Qd6+fTseeOABAEDr1q2RlJRUpXX069cPx44dw5EjR+ShW7duePTRR3HkyBE0a9YMBoMBcXFx8jL5+fnYs2cPevXqVZOy6wz2mSEiIrIfVU0WateuHf7zn//g/vvvR1xcHN544w0AwNWrVxEQEFCldfj4+KB9+/Y243Q6HQICAuTx06ZNw7x58xAZGYnIyEjMmzcPWq0W48aNq0nZdQb7zBAREdlPjcLM22+/jQcffBDvvPMOJk6ciE6dOgEANm/eLJ9+sofp06cjNzcXkydPRmpqKnr06IFt27bBx8fHbttwBZ5mIiIish9J1PA2tCaTCRkZGTb3hjl//jy0Wi2CgoLsVmBtZWRkQK/XIz09vc70n/njSjqG/usnBPlocGB2/9svQEREdIepzu93jfrM5Obmwmg0ykHmwoULWLJkCRISEupUkKmrrC0zN7PzYTLzkQZERES1UaMwM3z4cKxZswYAkJaWhh49emDRokUYMWIEli1bZtcC6yN/nRqSBJjMAqk5+a4uh4iIyK3VKMwcOnQI9957LwDgq6++QnBwMC5cuIA1a9bggw8+sGuB9ZGHUgF/reUZTewETEREVDs1CjM5OTlyJ9xt27Zh5MiRUCgU6NmzJy5cuGDXAusrXp5NRERkHzUKMy1atMCmTZtw6dIl/PDDD/Lzk1JSUupMJ9u6jmGGiIjIPmoUZl577TW8/PLLaNq0Ke6++25ERUUBsLTSdO7c2a4F1le8PJuIiMg+anSfmYceegi9e/dGUlKSfI8ZwHJX3wcffNBuxdVnbJkhIiKyjxqFGQAwGAwwGAy4fPkyJElCWFiYXW+YV9/xLsBERET2UaPTTGazGa+//jr0ej2aNGmCxo0bo0GDBnjjjTdgNpvtXWO9xJYZIiIi+6hRy8zs2bOxfPlyLFiwAPfccw+EEPj5558RGxuLvLw8vPXWW/aus95hnxkiIiL7qFGYWb16NT755BP5adkA0KlTJ4SFhWHy5MkMM1UQxJYZIiIiu6jRaaZbt26hdevWZca3bt0at27dqnVRd4JAb08AQHpuAYyFJhdXQ0RE5L5qFGY6deqEpUuXlhm/dOlSdOzYsdZF3Ql8vVRQKy1f/40sPtKAiIiopmp0mmnhwoW4//77sX37dkRFRUGSJMTHx+PSpUv47rvv7F1jvSRJEgJ9NLiSlovrmUaENfBydUlERERuqUYtM3369MHp06fx4IMPIi0tDbdu3cLIkSNx/PhxrFy50t411lsN2W+GiIio1mp8n5nQ0NAyHX1///13rF69GitWrKh1YXcC671mUjLzXFwJERGR+6pRywzZB+81Q0REVHsMMy7EMENERFR7DDMuxDBDRERUe9XqMzNy5MhKp6elpdWmljuO/Hwm3gWYiIioxqoVZvR6/W2nP/bYY7Uq6E7ClhkiIqLaq1aY4WXX9lXykQZCCEiS5OKKiIiI3A/7zLiQtWXGWGhGprHQxdUQERG5J4YZF/L0UMLH09I4xlNNRERENcMw42LsN0NERFQ7DDMuJl/RxDBDRERUIwwzLsaWGSIiotphmHExOczwXjNEREQ1wjDjYmyZISIiqh2GGRdjnxkiIqLaYZhxMbbMEBER1Q7DjIuxzwwREVHtMMy4mDXM3MwywmQWLq6GiIjI/TDMuFiATgOFBJgFcDObrTNERETVxTDjYkqFBH8d+80QERHVFMNMHcBOwERERDXHMFMHMMwQERHVHMNMHRDEK5qIiIhqjGGmDmDLDBERUc0xzNQBvAswERFRzTHM1AFsmSEiIqo5l4aZZcuWoWPHjvD19YWvry+ioqLw/fffy9OFEIiNjUVoaCi8vLwQHR2N48ePu7Bix+BdgImIiGrOpWGmUaNGWLBgAX799Vf8+uuvuO+++zB8+HA5sCxcuBCLFy/G0qVLcfDgQRgMBsTExCAzM9OVZdsdW2aIiIhqzqVhZtiwYRgyZAhatmyJli1b4q233oK3tzf2798PIQSWLFmC2bNnY+TIkWjfvj1Wr16NnJwcrFu3zpVl2501zGTmFSKvwOTiaoiIiNxLnekzYzKZsH79emRnZyMqKgqJiYlITk7GgAED5Hk0Gg369OmD+Ph4F1Zqfz4aFTQqy6Fg6wwREVH1qFxdwLFjxxAVFYW8vDx4e3tj48aNaNu2rRxYgoODbeYPDg7GhQsXKlyf0WiE0VgcCDIyMhxTuB1JkoRAHw0up+biepYR4f5aV5dERETkNlzeMtOqVSscOXIE+/fvxzPPPIOJEyfixIkT8nRJkmzmF0KUGVfS/Pnzodfr5SE8PNxhtUMIoNA+LSnsN0NERFQzLg8zarUaLVq0QLdu3TB//nx06tQJ77//PgwGAwAgOTnZZv6UlJQyrTUlzZo1C+np6fJw6dIlxxS+70Pg3ZbAz+/bZXW81wwREVHNuDzMlCaEgNFoREREBAwGA+Li4uRp+fn52LNnD3r16lXh8hqNRr7U2zo4hNIDyE4BLu6zy+qsLTMpDDNERETV4tI+M6+88goGDx6M8PBwZGZmYv369di9eze2bt0KSZIwbdo0zJs3D5GRkYiMjMS8efOg1Woxbtw4V5Zt0bin5fXSAcBUCChr91XyNBMREVHNuDTMXLt2DRMmTEBSUhL0ej06duyIrVu3IiYmBgAwffp05ObmYvLkyUhNTUWPHj2wbds2+Pj4uLJsi6C2gEYPGNOBa38AoXfVanUMM0RERDXj0jCzfPnySqdLkoTY2FjExsY6p6DqUCiB8LuBP+OAi/trH2a8eRdgIiKimqhzfWbcivVU08Xa3/fG2jJzgy0zRERE1cIwUxtNijoiX9xvuUy7FoJ8PQFYTjOJWq6LiIjoTsIwUxuhXQClGsi6BqQm1mpVDb3VAIB8kxkZuYX2qI6IiOiOwDBTGx6eQGhny/uL+2u1Ko1KCb2XBwDgelZebSsjIiK6YzDM1Ja138wF+/Wb4b1miIiIqo5hprYal+g3U0u8CzAREVH1MczUVvjdltebZ4DsG7VaFe81Q0REVH0MM7Wl9QcC21je1/LRBnKY4b1miIiIqoxhxh6aRFlea3mqiS0zRERE1ccwYw+NrWGmli0z7DNDRERUbQwz9mC9oinpdyA/u8arYcsMERFR9THM2IM+HPANA8yFwOVfa7wa+ZEG7DNDRERUZQwz9iBJJU411bzfjDXM3MzOR6HJbI/KiIiI6j2GGXuRHzpZ834zflo1lAoJQlgCDREREd0ew4y9WFtmLh8ETDV7tpJSISFAZ3lGE/vNEBERVQ3DjL0EtQU0eiA/C7h2rMarYSdgIiKi6mGYsReFAmjcw/LeDv1mGGaIiIiqhmHGnuzQbyaIdwEmIiKqFoYZe7L2m7mwDxCiRqtgywwREVH1MMzYU2gXQKkGslOAW+dqtAreBZiIiKh6GGbsycPTEmiAGvebCfTxBMAwQ0REVFUMM/ZWy34zfHI2ERFR9TDM2FuTXpbX2oYZtswQERFVCcOMvYXfbXm9+SeQdb3ai1vDTJaxEDn5Nbv5HhER0Z2EYcbevPwsN9ADgEvV7zejUyvh5aEEANzI5CMNiIiIbodhxhGs/WYuVP9UkyRJJfrN5NmzKiIionqJYcYRGrPfDBERkbMwzDiCtWUm6XcgP7vai/NeM0RERFXHMOMIDcIB30aAMAGXf6324myZISIiqjqGGUdpUvRogxqcarKGmRSGGSIiottimHGUWtw8jy0zREREVccw4yjWh05eOgiYqne/GLnPDO8CTEREdFsMM44S2Abw1AMF2UDy0eotypYZIiKiKmOYcRSFAgi3nmqq3s3zgnwtYeZGlhFms7B3ZURERPUKw4wj1bDfTIDOEmYKTALpuQX2roqIiKheYZhxJGu/mYv7AVH1Fha1SgE/rQcA9pshIiK6HYYZRwrrAig1QHYKcOtctRZlvxkiIqKqYZhxJJXGEmiAap9qCm3gBQDYeSrF3lURERHVKwwzjlbDfjOTejUFAKyOP4+z17PsXBQREVH9wTDjaNZ+M9V8gnZ0qyD0ax2EQrPAG1tOOKAwIiKi+oFhxtHC7wYgAbfOAlnVO2X0z6Ft4aGUsDvhOnaeuuaY+oiIiNwcw4yjefkBQW0t76t5v5mIhjo80TsCAPDGlpPILzTbuzoiIiK359IwM3/+fHTv3h0+Pj4ICgrCiBEjkJCQYDOPEAKxsbEIDQ2Fl5cXoqOjcfz4cRdVXEONa3bzPACY2rcFGnprkHgjG6viE+1cGBERkftzaZjZs2cPpkyZgv379yMuLg6FhYUYMGAAsrOz5XkWLlyIxYsXY+nSpTh48CAMBgNiYmKQmZnpwsqrqUkvy+vF+Gov6uPpgRmDWgEAPtjxJ1Iy8+xZGRERkduThKjG3dwc7Pr16wgKCsKePXvwl7/8BUIIhIaGYtq0aZgxYwYAwGg0Ijg4GG+//Taeeuqp264zIyMDer0e6enp8PX1dfQulC/9MvBeO0BSAjMvAhrvai1uNgs8+OHP+P1yOh7u2gjvPNzJQYUSERHVDdX5/a5TfWbS09MBAP7+/gCAxMREJCcnY8CAAfI8Go0Gffr0QXx8+a0cRqMRGRkZNoPL6RsB+nBAmIArv1Z7cYVCwpwH2gEAvvztMn6/lGbnAomIiNxXnQkzQgi8+OKL6N27N9q3bw8ASE5OBgAEBwfbzBscHCxPK23+/PnQ6/XyEB4e7tjCq8rab6aal2hbdWnsh5GdwwAAc785jjrUoEZERORSdSbMTJ06FUePHsVnn31WZpokSTafhRBlxlnNmjUL6enp8nDp0iWH1Ftt8nOaahZmAGDG4NbQqpU4dDENm45csVNhRERE7q1OhJlnn30Wmzdvxq5du9CoUSN5vMFgAIAyrTApKSllWmusNBoNfH19bYY6wRpmLv8KmGr2JOxgX09Mva8FAGDB96eQbSy0V3VERERuy6VhRgiBqVOnYsOGDdi5cyciIiJspkdERMBgMCAuLk4el5+fjz179qBXr17OLrd2AlsDng2Agmwg+ViNV/PEPRFo7K/FtQwjPtz9p/3qIyIiclMuDTNTpkzB2rVrsW7dOvj4+CA5ORnJycnIzc0FYDm9NG3aNMybNw8bN27EH3/8gUmTJkGr1WLcuHGuLL36FIoaP6epJE8PJf55fxsAwMd7E3HxZo49qiMiInJbLg0zy5YtQ3p6OqKjoxESEiIPn3/+uTzP9OnTMW3aNEyePBndunXDlStXsG3bNvj4+Liw8hqyQ5gBgJi2wbg3siHyC81481s+t4mIiO5sdeo+M45QJ+4zY3VxP7BiIKALBF4+A1TQibkqzlzLxKD398JkFlj7ZA/0jmxox0KJiIhcy23vM1PvhXYGlBog+zpw82ytVhUZ7IMJPZsAsFyqXWjic5uIiOjOxDDjTCoNENbV8r6Wp5oA4IX+LeGn9cCZlCys3X+h1usjIiJyRwwzzlaLh06Wptd64OWBluc2LY47jVvZ+bVeJxERkbthmHE2O9w8r6RHujdGmxBfZOQVYnFcwu0XICIiqmcYZpwt/G4AEnDrLJB5rdarUyokxA5rCwBY98tFnLhaB55FRURE5EQMM87m1QAItjw0Epdqf6oJAHo0C8D9HUNgFnxuExER3XkYZlzBjv1mrGYNbg2NSoFfEm/hu2PlP4STiIioPmKYcQVrv5kL8XZbZSM/LZ7u0xwAMO+7k8grMNlt3URERHUZw4wrWMNM8lHAmGm31T7dpzlC9Z64kpaL/+45Z7f1EhER1WUMM66gDwP0jQFhtjxF20681ErMGmJ5btOyPX/iSlqu3dZNRERUVzHMuIoD+s0AwNCOIbg7wh95BWbM/+6kXddNRERUFzHMuEoT6/1m7NdvBrA8aXzOsLZQSMCWo0k4kHjLrusnIiKqaxhmXMXab+byr4CpwK6rbheqxyN3NwYAxG4+DpOZl2oTEVH9xTDjKg1bAZ4NgIIcS0dgO3sppiV8PFU4kZSBzw9esvv6iYiI6gqGGVdRKIr7zVywz6MNSgrw1uCF/i0BAO9uS0B6rn1bf4iIiOoKhhlXsvNzmkqbENUELYK8cSs7H+9vP+OQbRAREbkaw4wryWFmP+CARxB4KBV4bajluU1r9p3Hnyn2u6cNERFRXcEw40qhdwFKDZBzA7h51iGb+EvLQPRvE4xCs8Dcb07wuU1ERFTvMMy4kkoDNOpmeW/nS7RLenVoG6iVCuw9cwM7TqY4bDtERESuwDDjag66eV5JTQJ0ePLeCADAG9+egLGQz20iIqL6g2HG1RzcCdhqSt8WCPLR4MLNHKz46bxDt0VERORMDDOu1qg7AAm4dQ7IvOawzXhrVJgxqDUAYOnOM0jJyHPYtoiIiJyJYcbVvBoAwe0t7x3cOvNg5zDcFd4A2fkmvL01waHbIiIichaGmbrACf1mAEChsDy3CQC+PnQZhy+mOnR7REREzsAwUxfIYcaxLTMA0LmxH0Z1aQQA+MdXR7Hh0GWk5eQ7fLtERESOonJ1AYTiTsDJRwFjJqDxcejmZgxqhbgTyfgzJQsvfvE7lAoJ3Zv6IaatAQPaBiPcX+vQ7RMREdmTJOr5XdQyMjKg1+uRnp4OX19fV5dTsSUdgLSLwISNQPP7HL65Czez8eWvlxF34hoSrtneGbi1wQcxbYMR0zYYHcL0kCTJ4fUQERGVVJ3fb4aZumLD34GjnwN9ZgB9X3Hqpi/ezEHcyWuIO5GMg+dTYTIX/5EI9tWgfxtLsIlqHgCNSunU2oiI6M7EMFOC24SZX1cAW14AIv4CTPzGZWWkZudjV0IK4k5cw57T15GTX3yDPW+NCn1aBiKmbTD6tgqCXuvhsjqJiKh+Y5gpwW3CTMop4MMegIcWmHkRULo+KOQVmLDv3E3EnbiG7SeuISXTKE9TKiT0iPCXW23Yz4aIiOyJYaYEtwkzZjPwTjMgNxX4606gUVdXV2TDbBY4eiUdcSeSEXfiGk5fy7KZ3trggwFtgxHT1oD2Yb7sZ0NERLXCMFOC24QZAFj3CHD6e2DAW0Cvqa6uplIXbmYj7sQ1xJ24hoPnb6FENxuE6D3Rv00w7msThKhmAfD0YD8bIiKqHoaZEtwqzPy0BNg+B2g9FHjkU1dXU2Wp2fnYecrSz+bHM7b9bDQqBXo1D0Df1kGIbhmExgE8HUVERLfHMFOCW4WZSweA5TGANgD4x1nADU/V5BWYsO/sTWw7cQ27E1KQlG77DKhmgTr0bRWE6FaBuDvCn1dHERFRuRhmSnCrMFNoBBY0BgrzgKm/Ag0jXV1RrQghcPpaFnYlpGDXqRT8diEVhSXOR2nVSvRq3hDRrQIR3SoQjfzYakNERBYMMyW4VZgBgJVDgAs/Aw/8C+jymKursauMvAL8fOYGdiWkYHfCdZurowCgZbA3ootabbo18YdaxadtEBHdqRhmSnC7MLPjDWDvu0CnccCDy1xdjcMIIXAiKQO7E65j16kUHLqYatOJ2FujQu8W1labIBj0nq4rloiInK46v998NlNdY31OkxMeOulKkiShXage7UL1mNK3BdJy8rG3qNXmx9PXcSMrH1uPJ2Pr8WQAQJsQX0S3CkTfVkHo0rgBVEq22hARkQVbZuqavHRgQRMAAngpAfAxuLoipzObBf64mo5dp65j9+kUHLmUhpJ/Sn09Vegd2RDtw/RoGeSDyGBvhPtpoVC4X4dpIiIqH08zleB2YQYAlvUGrh0DHl4FtHvQ1dW43K3sfPx4+rrcapOaU1BmHk8PBZoHeqNlsCXcRAb5oCVDDhGR2+JpJnfXJMoSZi7uZ5gB4K9TY0TnMIzoHAaTWeD3y2nYd/YmEpIzcSYlC2evZyGvwIzjVzNw/GqGzbIlQ06LIMtry2BvNPLTQsmQQ0RULzDM1EWNewIHPqr3/WZqQqmQ0KWxH7o09pPHFZrMuJSai9PXMnHmmiXgnL5WecjRqBRoEeSNyCBvRAb7WFp0grwR7s+QQ0TkblwaZn788Ue88847+O2335CUlISNGzdixIgR8nQhBObOnYuPPvoIqamp6NGjB/7973+jXbt2rivaGaydgJOPAcZMQOPj2nrqOJVSgYiGOkQ01GFgu+I+RiazwMVbOSUCTibOXMvCn9ezYCysOOQ0D/RGK4MP2ob4om2oL9qG+MJPp3b2bhERURW5NMxkZ2ejU6dOePzxxzFq1Kgy0xcuXIjFixdj1apVaNmyJd58803ExMQgISEBPj71+AfeNxRo0ARIuwAsvRsI62IZQrsAoZ0BrwaurtAtKBWSHHIGlMi/JrPApVs5lnCTkoUz1zLllhxjoRknkjJwIikDGw9fkZcJ1XvKwcbyqke4vxcfqElEVAfUmQ7AkiTZtMwIIRAaGopp06ZhxowZAACj0Yjg4GC8/fbbeOqpp6q0XrfsAAwAP70HbJ8LoJzD49/cNtyEdATUOqeXWN9YQ86ZlCycKgo0J5IycOFmTrnz+2hUaGMTcHzRMtiHN/sjIrIDt7yaqXSYOXfuHJo3b45Dhw6hc+fO8nzDhw9HgwYNsHr16nLXYzQaYTQW31k2IyMD4eHh7hdmAMsppqTfgSuHgKuHLK9pF8rOJymAwDZAWGdLwAnrAgS1A1Q8NWIPmXkFOJWciRNXM3D8ajpOJGXgdHIW8k3mMvN6KCW0CLI9RdU21Bd6Lw8XVE5E5L7qxdVMycmWm6UFBwfbjA8ODsaFC+X8oBeZP38+5s6d69DanEbjAzTtbRmssm8CSYeBK4eLA05WMpBy3DIcXmuZT6kGgtsXt+CEdQEatgQUfLBjdfl4eqB7U390b+ovjyswmXH2ehaOXylqwblqeU3PLcDJpAycTMrA14eK19HIz0sONu1C9Qhr4AWNhwKeHkpoVMWvHrwZIFG9ZTYLpOUW4FZ2ftFgxK3sAtzKNiLfJODpoYCnSglPDyW81MXvrf9WWD4XvfdQyvPz9hN1OMxYle6TIISotJ/CrFmz8OKLL8qfrS0z9YYuAGjR3zJYZVwtar0pEXDy0izvr5b4RfXQASGdigJOZyCwNaBraHlKt5ItB9XhoVSgtcEXrQ2+sPb2EkLgSlquHGysr5dTc+Vh24lrla5XqZCgUSlsAo71VVP6c9E/bGVfFfDTqdHQW1M0qOGnVfMfPCI7MxaacCs7Hzez8pGak2/z/mZ2Pm5l5eNW0fjUbMt4swPOhahVCniqSoWcovBjDUJeHqWCkEoBT7VSDkyeJeYrGZ681EqbkKVRKerkvyV1NswYDJarUpKTkxESEiKPT0lJKdNaU5JGo4FGo3F4fXWKb6hlaDPU8lkIIDWxOOBcOWQ5XVWQDVyMtwyleeotoUbbsCjg+Jd4bx0fUPxerQPY+dWGJElo5KdFIz8tBpS4qio9p0Duf2MNODeyjDAWmJBXaEZ+YfHpKpNZICffhJx8E4CyNwesKaVCgr8ccNQI9NagoY8GAdZxPsXj/XVqPi6CnEoIAbMACs1mFJoECs0CJrNAocksvy8wmS3jzKJoHnPR+KJ5zcXTzWYBk7CMLzmu0CxgFpblzaJ4O9bBOs46rzxNCOQVmJBqbVHJsQSV7HxTjfbX11MFf53aZlCrFMgrMCOvwIS8AjOMhSbk5puQV2iyHV9gGVdgKk5F+UX/jmTkFdrrkFRKIwen4tAzuns4nuwd4ZTtl6fOhpmIiAgYDAbExcXJfWby8/OxZ88evP322y6uro6TJMC/mWXo8JBlnNkE3Dhdtv9Nzi0AwvIYhbx04Na5qm1D5VkUbAIqDjzaAEso8vK3vNbl1h8hgPwsy/eRewsoNAIeWktosw4e2hqdptNrPRDVPABRzQPKnW42C+SbzDAW/QOWV+tXy/8Wb2Tl40aWEWk5BTCZBa5nGnG91JPKyyNJgJ9WjYbeJVt3NGjoY/nsr1VDoYD8iAn5Vf4qRanP8pdcwfzWz8XTRdF6LO+LXgVgFkVzlRyPovFF71E0j9ksitZTvD7A8r9YrVoFb40SOo2q6L0KOo0SOrUKOo3KbTpxm82i1I+dCcbC4h++vEKT5cevxPS8QsuftbyiPyulfyDNRT/qZlH8vVo+W8YJYf3hLw4hJaeVXNZstl1PycBROpC4K5VCgp9OjQCdpQXU37v4fUBRq2iAzjLeX6uGn05tl9PJJrOwOaZ5BZbwYywVfvIKSgciU6lp5hLLFY/PrSQ8GQvNMBaakZ5bXE9aTn6t96k2XBpmsrKy8Oeff8qfExMTceTIEfj7+6Nx48aYNm0a5s2bh8jISERGRmLevHnQarUYN26cC6t2UwolENTGMnR+tHi82QTkpgE5N4DsG0DOzaL3N0u8t46/aXlvMgKFeUDGFctQVRpfwMuvVMixvq9gvIdX9fe1MB/ITbWEEms4yblValxq8TTre3MVWkJUXrYBRw465YyrbJpnA0sI9PCCQiHBU2FpwgXsH/gKTGbcLAo2lqHofWapz1lG3Mq2NINbz+mfvpZl93rcgVqpgLYo3HhrVNBqlJbAUxR2dEVByFujglZd/N5LrYQQwvI/ZZNAQaEZ+SYzCkzmonFmFBQK5JssPw7F44rmMZmRXyjKGVf83vpjYywwl9sJvT5RSJb7SKkUEpQKCR5KBZQKCSqFBJVSgkpR/Lnkq+2ggFKC5VWBCsZZXlUKBRSSZd0KSSqeJklQqxTw13nAX6cp8aqGr6fKJbdoUCqkoj+LzvkZLzSZ5dCUVzogF5gR5leDf6vtyKVXM+3evRt9+/YtM37ixIlYtWqVfNO8//73vzY3zWvfvn2Vt+G2l2bXVUIA+dmWkJNzsyj03CgOOjk3LMHAGoByb1nCUnmXmFeFyqvolFeJFh5tgCUYGTNsQ0lOUVjJr8UPsFJTHKIKci37mp8FCAf9aKh9LK1ZusCioWGJ96U+e/kDSsf+w2UyC6TmWMNOcci5XuJzWm5BcXNK0T/i1n/Krf+mF3+uaLrtjKWnKyQJkmSZr+w4y3qlovlt3hfNryiascy8APJNZmQZTcgxFiLLWIjs/ELkGE3IMhbCWOi+4cBDKRX1kSjZN8K2r0PFnUktr6qiH3VJkqCUJCgU1u9dgqLoe1UUfefWH3vre+t0SZ6vaJzC8l4CKg0kHkprIFFApbRun6ey72RueWm2ozDM1AHW1h9rq4g15JR5f8u2NcVcm/O/kuXmgl6lgpCXX9F7P9tpXn5FIUZbti+QEJbTTtZgk58NFOQUv88v+T7b0jcpv4KhIBswZlk6aJuq2ywrWWq0hhttRSHI0uIDhapoUBa9ehR/Zn+nchWazMjONyHbWGgZit5nGQuRk1+ILKPlsyUIFc2XXzSv0YScgkIoJcsPtrro6jQPpaLoSjXb8WqVAmqlosQ4yWacR9F7tapouaJx5V3RolEp+RgOqnfqxaXZVI8olEWtD+X3GSmXEJaWl9Ihxxp+8jIsl67bBJUSgcVTb7/L0CUJ8PC0DNXZh8pY9y+76DRe9vWioeT7Ep9zbgIQxaf7rp+q3fYlpaUPk03YsQaeEp+VpT4rVJb+UgEtgMBWltOWga0t33s9oFIqoPdS8L5ARG6GYYbqJkmyBBJPPeDvuh7yDlNy/wKa335+s8lySq28oGPz/kZxvyZTASAquNpCmIDCml2JAQA4u8P2s3ewJdwEtgGCWhe/evmVvzwRkR0xzBC5A4Wy6FRSQwBtqr6cEJYgZC60dHA2FxZ/NpX6XOl06/tCy2myGwlAyingegKQfhHIumYZEn+03b53sKXlxtqCE9TGEnoYcojIjhhmiOozSbJ0GlaqAHg6ZhvGTOD6aeD6SSDlpCXgXD8FpF8qEXL22C7jbbBtwQksGvgQVecxFRR1oL9ZfApXqQaC2wL6cParIrfCMENEtaPxARp1tQwlGTOLg03KyaLXU0DGZcsjOLKSgXO7bZfxCQEaNLb8qMp9djwsYczap0dpfa3uNI+y61Sqyy5X3rbl+Tzq5o98obFE37Kb5Xeulz/ftFz5Z0yveH2eesvjUILbFb22t7SqqbXO2yeiauDVTETkXHkZlhs4ygGnqDUn47KrK6saufO0Nfx42AYi+YoxRdGrstSrorhTdelxNvMqSn1WWW4RUN6VgDW+HUHJq/4CLFfc3Ugo/0pCSQH4N7cEHEP74pCjb1Q3Ax65PV6aXQLDDJGbyMuwhJrMpKI+OqaifjsFxf135M8l+vhUNM1Uop9PedMqXHeJabW6PYCTSYrim0/KN54s/dnf9rNXg7JX/RXmWwLNteNA8jHg2h9A8h+We0iVR27FaV8UctoBQW1rdsNLohIYZkpgmCGiGhOiKOjkVx56TKWClTABZnPRq6nsa3njhLm483XpcdbPklTiXkklw4mf5a7SCgc+hiHzmiXYWMPNteO3b8Up2YJjaG+pVVKUGtiqQ+VjmCmBYYaIyEEKjZbWtGvHbYNORa04FZJsw03pwAOp1Phy5lEoLR3LGzQuMYQDDZpYToWp7rAHENcDvGkeERE5nkoDhHS0DFZCWK5gK9mCc+0PSz+pCk/bCUvrU0X3Raqq1PPApf3lT7N2LteHlwo8RWHHw0FX+5FTMMwQEZH9SBLgY7AMLfoXjzcVWB5QK8xFjzIv+Vo0oNRneZ5yxpee11RgefBt2kUg7VLR60XLLQIKcix9sTKTgEu/lF+3d7BtyNEXtepYW3jYB6hOY5ghIiLHs1755Wyi6DEgaRfKDzqpFyzPTLPeE+nywfLX4+VneTCsWlc0aAG1t+W9R4n35Y7XFk3zLhpXtA57PXKFGGaIiKgek6Tiu2eHdS07XQjLzQPTLpQNOtb3xgzLPLmp9q1N5VkcbDS+RZ27/Uo9FLfE+5LTXBEM6zCGGSIiunNJRU+j1/oDoZ3Lnyc3DchMttyHJz/LctrK+j4/p4Lx2UXTit6XnCbMlvUW5lmGnJvVr1vtY7n0vnTIqSgASYpyrqIrLHHVXWEVxpdetsQVd6GdgcY9anwYaothhoiIqDJeDez3qA0hLAGmZNDJz7bckTmnqPUn91bRoyZulXqfCuSlWdaTn2kZ0i7ap67a6v0iwwwREdEdQZIsnYk9vABdQPWXN5ssLUW3Cz3Wu0Nb5xXmEnebVpW6C7XC9o7UJe9GXen4EnezDm5n72+qWhhmiIiI3IVCaQlBNQlC9ZgDbxdJRERE5HgMM0REROTWGGaIiIjIrTHMEBERkVtjmCEiIiK3xjBDREREbo1hhoiIiNwawwwRERG5NYYZIiIicmsMM0REROTWGGaIiIjIrTHMEBERkVtjmCEiIiK3xjBDREREbk3l6gIcTQgBAMjIyHBxJURERFRV1t9t6+94Zep9mMnMzAQAhIeHu7gSIiIiqq7MzEzo9fpK55FEVSKPGzObzbh69Sp8fHwgSZJd152RkYHw8HBcunQJvr6+dl13XcN9rb/upP3lvtZfd9L+3in7KoRAZmYmQkNDoVBU3ium3rfMKBQKNGrUyKHb8PX1rdd/oErivtZfd9L+cl/rrztpf++Efb1di4wVOwATERGRW2OYISIiIrfGMFMLGo0Gc+bMgUajcXUpDsd9rb/upP3lvtZfd9L+3kn7WlX1vgMwERER1W9smSEiIiK3xjBDREREbo1hhoiIiNwawwwRERG5NYaZSnz44YeIiIiAp6cnunbtir1791Y6/549e9C1a1d4enqiWbNm+M9//uOkSmtn/vz56N69O3x8fBAUFIQRI0YgISGh0mV2794NSZLKDKdOnXJS1TUTGxtbpmaDwVDpMu56XAGgadOm5R6nKVOmlDu/Ox3XH3/8EcOGDUNoaCgkScKmTZtspgshEBsbi9DQUHh5eSE6OhrHjx+/7Xq//vprtG3bFhqNBm3btsXGjRsdtAdVV9m+FhQUYMaMGejQoQN0Oh1CQ0Px2GOP4erVq5Wuc9WqVeUe67y8PAfvze3d7thOmjSpTN09e/a87Xrd7dgCKPcYSZKEd955p8J11uVj6ygMMxX4/PPPMW3aNMyePRuHDx/Gvffei8GDB+PixYvlzp+YmIghQ4bg3nvvxeHDh/HKK6/gueeew9dff+3kyqtvz549mDJlCvbv34+4uDgUFhZiwIAByM7Ovu2yCQkJSEpKkofIyEgnVFw77dq1s6n52LFjFc7rzscVAA4ePGizr3FxcQCAhx9+uNLl3OG4Zmdno1OnTli6dGm50xcuXIjFixdj6dKlOHjwIAwGA2JiYuTntZVn3759GDNmDCZMmIDff/8dEyZMwOjRo/HLL784ajeqpLJ9zcnJwaFDh/Dqq6/i0KFD2LBhA06fPo0HHnjgtuv19fW1Oc5JSUnw9PR0xC5Uy+2OLQAMGjTIpu7vvvuu0nW647EFUOb4rFixApIkYdSoUZWut64eW4cRVK67775bPP300zbjWrduLWbOnFnu/NOnTxetW7e2GffUU0+Jnj17OqxGR0lJSREAxJ49eyqcZ9euXQKASE1NdV5hdjBnzhzRqVOnKs9fn46rEEI8//zzonnz5sJsNpc73V2PKwCxceNG+bPZbBYGg0EsWLBAHpeXlyf0er34z3/+U+F6Ro8eLQYNGmQzbuDAgeKRRx6xe801VXpfy3PgwAEBQFy4cKHCeVauXCn0er19i3OA8vZ34sSJYvjw4dVaT305tsOHDxf33XdfpfO4y7G1J7bMlCM/Px+//fYbBgwYYDN+wIABiI+PL3eZffv2lZl/4MCB+PXXX1FQUOCwWh0hPT0dAODv73/beTt37oyQkBD069cPu3btcnRpdnHmzBmEhoYiIiICjzzyCM6dO1fhvPXpuObn52Pt2rV44oknbvvQVXc8riUlJiYiOTnZ5thpNBr06dOnwr/DQMXHu7Jl6qL09HRIkoQGDRpUOl9WVhaaNGmCRo0aYejQoTh8+LBzCrSD3bt3IygoCC1btsTf/vY3pKSkVDp/fTi2165dw7fffosnn3zytvO687GtCYaZcty4cQMmkwnBwcE244ODg5GcnFzuMsnJyeXOX1hYiBs3bjisVnsTQuDFF19E79690b59+wrnCwkJwUcffYSvv/4aGzZsQKtWrdCvXz/8+OOPTqy2+nr06IE1a9bghx9+wMcff4zk5GT06tULN2/eLHf++nJcAWDTpk1IS0vDpEmTKpzHXY9rada/p9X5O2xdrrrL1DV5eXmYOXMmxo0bV+lDCFu3bo1Vq1Zh8+bN+Oyzz+Dp6Yl77rkHZ86ccWK1NTN48GB8+umn2LlzJxYtWoSDBw/ivvvug9ForHCZ+nBsV69eDR8fH4wcObLS+dz52NZUvX9qdm2U/t+rEKLS/9GWN3954+uyqVOn4ujRo/jpp58qna9Vq1Zo1aqV/DkqKgqXLl3Cu+++i7/85S+OLrPGBg8eLL/v0KEDoqKi0Lx5c6xevRovvvhiucvUh+MKAMuXL8fgwYMRGhpa4TzuelwrUt2/wzVdpq4oKCjAI488ArPZjA8//LDSeXv27GnTafaee+5Bly5d8K9//QsffPCBo0utlTFjxsjv27dvj27duqFJkyb49ttvK/2hd+djCwArVqzAo48+etu+L+58bGuKLTPlaNiwIZRKZZnEnpKSUibZWxkMhnLnV6lUCAgIcFit9vTss89i8+bN2LVrFxo1alTt5Xv27Ol2yV+n06FDhw4V1l0fjisAXLhwAdu3b8df//rXai/rjsfVeoVadf4OW5er7jJ1RUFBAUaPHo3ExETExcVV2ipTHoVCge7du7vdsQYsLYpNmjSptHZ3PrYAsHfvXiQkJNTo77A7H9uqYpgph1qtRteuXeUrP6zi4uLQq1evcpeJiooqM/+2bdvQrVs3eHh4OKxWexBCYOrUqdiwYQN27tyJiIiIGq3n8OHDCAkJsXN1jmU0GnHy5MkK63bn41rSypUrERQUhPvvv7/ay7rjcY2IiIDBYLA5dvn5+dizZ0+Ff4eBio93ZcvUBdYgc+bMGWzfvr1GQVsIgSNHjrjdsQaAmzdv4tKlS5XW7q7H1mr58uXo2rUrOnXqVO1l3fnYVpmreh7XdevXrxceHh5i+fLl4sSJE2LatGlCp9OJ8+fPCyGEmDlzppgwYYI8/7lz54RWqxUvvPCCOHHihFi+fLnw8PAQX331lat2ocqeeeYZodfrxe7du0VSUpI85OTkyPOU3t/33ntPbNy4UZw+fVr88ccfYubMmQKA+Prrr12xC1X20ksvid27d4tz586J/fv3i6FDhwofH596eVytTCaTaNy4sZgxY0aZae58XDMzM8Xhw4fF4cOHBQCxePFicfjwYfkKngULFgi9Xi82bNggjh07JsaOHStCQkJERkaGvI4JEybYXKH4888/C6VSKRYsWCBOnjwpFixYIFQqldi/f7/T96+kyva1oKBAPPDAA6JRo0biyJEjNn+HjUajvI7S+xobGyu2bt0qzp49Kw4fPiwef/xxoVKpxC+//OKKXbRR2f5mZmaKl156ScTHx4vExESxa9cuERUVJcLCwurdsbVKT08XWq1WLFu2rNx1uNOxdRSGmUr8+9//Fk2aNBFqtVp06dLF5lLliRMnij59+tjMv3v3btG5c2ehVqtF06ZNK/yDV9cAKHdYuXKlPE/p/X377bdF8+bNhaenp/Dz8xO9e/cW3377rfOLr6YxY8aIkJAQ4eHhIUJDQ8XIkSPF8ePH5en16bha/fDDDwKASEhIKDPNnY+r9TLy0sPEiROFEJbLs+fMmSMMBoPQaDTiL3/5izh27JjNOvr06SPPb/Xll1+KVq1aCQ8PD9G6des6EeQq29fExMQK/w7v2rVLXkfpfZ02bZpo3LixUKvVIjAwUAwYMEDEx8c7f+fKUdn+5uTkiAEDBojAwEDh4eEhGjduLCZOnCguXrxos476cGyt/vvf/wovLy+RlpZW7jrc6dg6iiREUW9GIiIiIjfEPjNERETk1hhmiIiIyK0xzBAREZFbY5ghIiIit8YwQ0RERG6NYYaIiIjcGsMMERERuTWGGSK6I0iShE2bNrm6DCJyAIYZInK4SZMmQZKkMsOgQYNcXRoR1QMqVxdARHeGQYMGYeXKlTbjNBqNi6ohovqELTNE5BQajQYGg8Fm8PPzA2A5BbRs2TIMHjwYXl5eiIiIwJdffmmz/LFjx3DffffBy8sLAQEB+Pvf/46srCybeVasWIF27dpBo9EgJCQEU6dOtZl+48YNPPjgg9BqtYiMjMTmzZvlaampqXj00UcRGBgILy8vREZGlglfRFQ3McwQUZ3w6quvYtSoUfj9998xfvx4jB07FidPngQA5OTkYNCgQfDz88PBgwfx5ZdfYvv27TZhZdmyZZgyZQr+/ve/49ixY9i8eTNatGhhs425c+di9OjROHr0KIYMGYJHH30Ut27dkrd/4sQJfP/99zh58iSWLVuGhg0bOu8LIKKac/WTLomo/ps4caJQKpVCp9PZDK+//roQwvLk9qefftpmmR49eohnnnlGCCHERx99JPz8/ERWVpY8/dtvvxUKhUIkJycLIYQIDQ0Vs2fPrrAGAOKf//yn/DkrK0tIkiS+//57IYQQw4YNE48//rh9dpiInIp9ZojIKfr27Ytly5bZjPP395ffR0VF2UyLiorCkSNHAAAnT55Ep06doNPp5On33HMPzGYzEhISIEkSrl69in79+lVaQ8eOHeX3Op0OPj4+SElJAQA888wzGDVqFA4dOoQBAwZgxIgR6NWrV432lYici2GGiJxCp9OVOe1zO5IkAQCEEPL78ubx8vKq0vo8PDzKLGs2mwEAgwcPxoULF/Dtt99i+/bt6NevH6ZMmYJ33323WjUTkfOxzwwR1Qn79+8v87l169YAgLZt2+LIkSPIzs6Wp//8889QKBRo2bIlfHx80LRpU+zYsaNWNQQGBmLSpElYu3YtlixZgo8++qhW6yMi52DLDBE5hdFoRHJyss04lUold7L98ssv0a1bN/Tu3RuffvopDhw4gOXLlwMAHn30UcyZMwcTJ05EbGwsrl+/jmeffRYTJkxAcHAwACA2NhZPP/00goKCMHjwYGRmZuLnn3/Gs88+W6X6XnvtNXTt2hXt2rWD0WjEli1b0KZNGzt+A0TkKAwzROQUW7duRUhIiM24Vq1a4dSpUwAsVxqtX78ekydPhsFgwKeffoq2bdsCALRaLX744Qc8//zz6N69O7RaLUaNGoXFixfL65o4cSLy8vLw3nvv4eWXX0bDhg3x0EMPVbk+tVqNWbNm4fz58/Dy8sK9996L9evX22HPicjRJCGEcHURRHRnkyQJGzduxIgRI1xdChG5IfaZISIiIrfGMENERERujX1miMjleLabiGqDLTNERETk1hhmiIiIyK0xzBAREZFbY5ghIiIit8YwQ0RERG6NYYaIiIjcGsMMERERuTWGGSIiInJrDDNERETk1v4fPhaN3+fhGBQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attention Mechanism - MAE: 8.16, Error%: 11.22%\n",
      "Transfer Learning - MAE: 9.64, Error%: 13.42%\n"
     ]
    }
   ],
   "source": [
    "#9 CNN WITH SELF-ATTENTION MECHANISM OUTPERFORMING TRANSFER MODEL (TRAINING ON GROND TRUTH)\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.utils.data as data\n",
    "import torchvision.models as models\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import os\n",
    "from torchvision import transforms\n",
    "from scipy.signal import butter, filtfilt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error, precision_recall_fscore_support\n",
    "import pandas as pd\n",
    "\n",
    "# Dataset paths\n",
    "TEXT_DATASET_PATH = \"C:/Users/hp/Desktop/DatasetVideoFiles/predata/0_subject\"\n",
    "GROUND_TRUTH_PATH = \"C:/Users/hp/Desktop/DatasetVideoFiles/predata/PPG-BP dataset.xlsx\"\n",
    "\n",
    "# Load ground truth HR data\n",
    "df = pd.read_excel(GROUND_TRUTH_PATH, sheet_name=\"cardiovascular dataset\")\n",
    "df.columns = [\"Num\", \"subject_ID\", \"Sex\", \"Age\", \"Height(cm)\", \"Weight(kg)\",\n",
    "              \"Systolic_BP\", \"Diastolic_BP\", \"Heart_Rate(bpm)\", \"BMI\",\n",
    "              \"Hypertension\", \"Diabetes\", \"cerebral infarction\", \"cerebrovascular disease\"]\n",
    "df = df.iloc[1:].reset_index(drop=True)\n",
    "\n",
    "# Convert medical conditions to binary\n",
    "condition_columns = [\"Hypertension\", \"Diabetes\", \"cerebral infarction\", \"cerebrovascular disease\"]\n",
    "df[condition_columns] = df[condition_columns].applymap(lambda x: 1 if str(x).strip().lower() in [\"yes\", \"1\", \"true\"] else 0)\n",
    "df[condition_columns] = df[condition_columns].fillna(0).astype(int)\n",
    "ground_truth_hr = df[\"Heart_Rate(bpm)\"].astype(float).values\n",
    "\n",
    "# Bandpass filter to remove noise\n",
    "def bandpass_filter(signal, lowcut=0.5, highcut=3.0, fs=100, order=3):\n",
    "    nyquist = 0.5 * fs\n",
    "    low, high = lowcut / nyquist, highcut / nyquist\n",
    "    b, a = butter(order, [low, high], btype='band')\n",
    "    return filtfilt(b, a, signal)\n",
    "\n",
    "# Load PPG signals and HR labels\n",
    "def load_ppg_signals(folder):\n",
    "    signals, hr_values, conditions = [], [], []\n",
    "    file_list = sorted(os.listdir(folder))\n",
    "    num_hr_values = len(ground_truth_hr)\n",
    "\n",
    "    for i, file in enumerate(file_list):\n",
    "        if file.endswith(\".txt\"):\n",
    "            if i >= num_hr_values:\n",
    "                break\n",
    "            \n",
    "            filepath = os.path.join(folder, file)\n",
    "            data = np.loadtxt(filepath)\n",
    "            if len(data) >= 2100:\n",
    "                data = bandpass_filter(data[:1800])\n",
    "                data = (data - np.mean(data)) / np.std(data)  # Normalize\n",
    "\n",
    "                segments = np.split(data, 3)  # Split into 3 segments\n",
    "                signals.extend(segments)\n",
    "                hr_values.extend([ground_truth_hr[i]] * 3)\n",
    "                conditions.extend([df.iloc[i][condition_columns].values] * 3)\n",
    "\n",
    "    return np.array(signals).reshape(-1, 1, 600), np.array(hr_values), np.array(conditions, dtype=np.float32)\n",
    "\n",
    "ppg_signals, hr_labels, condition_labels = load_ppg_signals(TEXT_DATASET_PATH)\n",
    "\n",
    "# Train-test split\n",
    "train_ppg, test_ppg, train_hr, test_hr, train_conditions, test_conditions = train_test_split(\n",
    "    ppg_signals, hr_labels, condition_labels, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert to PyTorch tensors\n",
    "train_ppg, test_ppg = map(lambda x: torch.tensor(x, dtype=torch.float32), (train_ppg, test_ppg))\n",
    "train_hr, test_hr = map(lambda x: torch.tensor(x, dtype=torch.float32).view(-1, 1), (train_hr, test_hr))\n",
    "train_conditions, test_conditions = map(lambda x: torch.tensor(x, dtype=torch.float32), (train_conditions, test_conditions))\n",
    "\n",
    "train_loader = data.DataLoader(list(zip(train_ppg, train_hr, train_conditions)), batch_size=16, shuffle=True)\n",
    "test_loader = data.DataLoader(list(zip(test_ppg, test_hr, test_conditions)), batch_size=16, shuffle=False)\n",
    "\n",
    "\n",
    "\n",
    "# Use previously loaded dataset variables\n",
    "train_loader = train_loader \n",
    "test_loader = test_loader  \n",
    "\n",
    "# Define CNN with Attention Mechanism (Squeeze-and-Excitation Block)\n",
    "class SEBlock(nn.Module):\n",
    "    def __init__(self, channels, reduction=16):\n",
    "        super(SEBlock, self).__init__()\n",
    "        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Sequential(\n",
    "            nn.Linear(channels, channels // reduction),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(channels // reduction, channels),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        batch, channels, _ = x.shape\n",
    "        y = self.global_avg_pool(x).view(batch, channels)\n",
    "        y = self.fc(y).view(batch, channels, 1)\n",
    "        return x * y\n",
    "\n",
    "class CNNWithAttention(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNNWithAttention, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(1, 32, kernel_size=5, padding=2)\n",
    "        self.conv2 = nn.Conv1d(32, 64, kernel_size=5, padding=2)\n",
    "        self.se1 = SEBlock(64)\n",
    "        self.conv3 = nn.Conv1d(64, 128, kernel_size=5, padding=2)\n",
    "        self.se2 = SEBlock(128)\n",
    "        self.global_avg_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc_hr = nn.Linear(128, 1)\n",
    "        self.fc_cond = nn.Linear(128, 4)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.conv1(x))\n",
    "        x = torch.relu(self.conv2(x))\n",
    "        x = self.se1(x)\n",
    "        x = torch.relu(self.conv3(x))\n",
    "        x = self.se2(x)\n",
    "        x = self.global_avg_pool(x).squeeze(-1)\n",
    "        hr_output = self.fc_hr(x)\n",
    "        cond_output = torch.softmax(self.fc_cond(x), dim=1)\n",
    "        return hr_output, cond_output\n",
    "\n",
    "# Define CNN with Transfer Learning (Using ResNet as Feature Extractor)\n",
    "class CNNWithTransferLearning(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(CNNWithTransferLearning, self).__init__()\n",
    "        \n",
    "        # Load ResNet18 model\n",
    "        self.resnet = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)\n",
    "        \n",
    "        # Modify first conv layer to accept 1-channel input instead of 3-channel RGB\n",
    "        self.resnet.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)\n",
    "        \n",
    "        # Modify fully connected layer for the new task\n",
    "        self.resnet.fc = nn.Linear(self.resnet.fc.in_features, 128)\n",
    "\n",
    "        # Output layers\n",
    "        self.fc_hr = nn.Linear(128, 1)\n",
    "        self.fc_cond = nn.Linear(128, 4)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Ensure input is 4D [batch_size, channels, height, width]\n",
    "        x = x.unsqueeze(1)  # Convert [batch_size, 1, seq_length] → [batch_size, 1, seq_length, 1]\n",
    "        x = torch.nn.functional.interpolate(x, size=(64, 64), mode=\"bilinear\", align_corners=False)  # Resize to 64x64\n",
    "        \n",
    "        x = self.resnet(x)  # Pass through ResNet\n",
    "        \n",
    "        hr_output = self.fc_hr(x)  # Predict HR\n",
    "        cond_output = torch.softmax(self.fc_cond(x), dim=1)  # Predict condition\n",
    "        \n",
    "        return hr_output, cond_output\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Define training function\n",
    "def train_model(model, train_loader, num_epochs=20, lr=0.001):\n",
    "    criterion_hr = nn.HuberLoss()\n",
    "    criterion_cond = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "    loss_history = []\n",
    "    \n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        for signals, hr_values, cond_values in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            hr_pred, cond_pred = model(signals)\n",
    "            loss_hr = criterion_hr(hr_pred, hr_values)\n",
    "            loss_cond = criterion_cond(cond_pred, torch.argmax(cond_values, dim=1))\n",
    "            loss = loss_hr + loss_cond\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            running_loss += loss.item()\n",
    "        \n",
    "        loss_history.append(running_loss / len(train_loader))\n",
    "        print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {running_loss/len(train_loader):.6f}\")\n",
    "    \n",
    "    return loss_history\n",
    "\n",
    "# Function to evaluate model\n",
    "def evaluate_model(model, test_loader):\n",
    "    model.eval()\n",
    "    hr_preds, hr_truths, cond_preds, cond_truths = [], [], [], []\n",
    "    with torch.no_grad():\n",
    "        for signals, hr_values, cond_values in test_loader:\n",
    "            hr_pred, cond_pred = model(signals)\n",
    "            hr_preds.extend(hr_pred.numpy())\n",
    "            hr_truths.extend(hr_values.numpy())\n",
    "            cond_preds.extend(torch.argmax(cond_pred, dim=1).numpy())\n",
    "            cond_truths.extend(torch.argmax(cond_values, dim=1).numpy())\n",
    "    \n",
    "    mae = mean_absolute_error(hr_truths, hr_preds)\n",
    "    error_percentage = np.mean(np.abs((np.array(hr_preds) - np.array(hr_truths)) / np.array(hr_truths))) * 100\n",
    "    return mae, error_percentage\n",
    "\n",
    "# Train both models\n",
    "\n",
    "# Train and evaluate CNN with Attention\n",
    "model_attention = CNNWithAttention()\n",
    "loss_attention = train_model(model_attention, train_loader)\n",
    "mae_attention, error_attention = evaluate_model(model_attention, test_loader)\n",
    "\n",
    "# Train and evaluate CNN with Transfer Learning\n",
    "model_transfer = CNNWithTransferLearning()\n",
    "loss_transfer = train_model(model_transfer, train_loader)\n",
    "mae_transfer, error_transfer = evaluate_model(model_transfer, test_loader)\n",
    "\n",
    "# Plot loss comparison\n",
    "plt.plot(loss_attention, label='Attention Mechanism')\n",
    "plt.plot(loss_transfer, label='Transfer Learning')\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.title(\"Loss Function Comparison\")\n",
    "plt.show()\n",
    "\n",
    "# Print results\n",
    "print(f\"Attention Mechanism - MAE: {mae_attention:.2f}, Error%: {error_attention:.2f}%\")\n",
    "print(f\"Transfer Learning - MAE: {mae_transfer:.2f}, Error%: {error_transfer:.2f}%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2e15f5a-72ab-406c-ba33-d07f6737a2e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#9.2 SAVING MODEL\n",
    "torch.save(model.state_dict(), 'model_weights.pth')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cbad140a-8107-43f8-bccd-9f067d462e28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20, Train Loss: 90.1122, Val Loss: 86.0710\n",
      "Epoch 2/20, Train Loss: 86.9761, Val Loss: 81.2250\n",
      "Epoch 3/20, Train Loss: 70.6141, Val Loss: 49.1150\n",
      "Epoch 4/20, Train Loss: 35.5777, Val Loss: 17.7680\n",
      "Epoch 5/20, Train Loss: 34.2710, Val Loss: 14.1638\n",
      "Epoch 6/20, Train Loss: 31.2397, Val Loss: 16.1592\n",
      "Epoch 7/20, Train Loss: 29.1933, Val Loss: 14.2686\n",
      "Epoch 8/20, Train Loss: 25.3508, Val Loss: 19.1232\n",
      "Epoch 9/20, Train Loss: 25.0203, Val Loss: 17.2078\n",
      "Epoch 10/20, Train Loss: 22.6284, Val Loss: 15.8602\n",
      "Epoch 11/20, Train Loss: 22.3338, Val Loss: 17.3630\n",
      "Epoch 12/20, Train Loss: 22.2374, Val Loss: 22.7279\n",
      "Epoch 13/20, Train Loss: 20.1676, Val Loss: 20.4113\n",
      "Epoch 14/20, Train Loss: 21.2367, Val Loss: 15.4282\n",
      "Epoch 15/20, Train Loss: 19.8527, Val Loss: 18.7456\n",
      "Epoch 16/20, Train Loss: 20.9442, Val Loss: 15.0973\n",
      "Epoch 17/20, Train Loss: 20.3526, Val Loss: 14.4867\n",
      "Epoch 18/20, Train Loss: 19.5386, Val Loss: 19.2208\n",
      "Epoch 19/20, Train Loss: 18.4175, Val Loss: 15.0258\n",
      "Epoch 20/20, Train Loss: 19.5108, Val Loss: 14.0778\n",
      "\n",
      "===== Per-Video Heart Rate Prediction =====\n",
      "1.mp4: Predicted = 70.09 bpm, Actual = 88.00 bpm, Error = 17.91 bpm\n",
      "10.mp4: Predicted = 67.84 bpm, Actual = 83.00 bpm, Error = 15.16 bpm\n",
      "11.mp4: Predicted = 59.00 bpm, Actual = 86.00 bpm, Error = 27.00 bpm\n",
      "12.mp4: Predicted = 51.73 bpm, Actual = 87.00 bpm, Error = 35.27 bpm\n",
      "13.mp4: Predicted = 68.76 bpm, Actual = 83.00 bpm, Error = 14.24 bpm\n",
      "14.mp4: Predicted = 106.57 bpm, Actual = 73.00 bpm, Error = 33.57 bpm\n",
      "15.mp4: Predicted = 91.25 bpm, Actual = 59.00 bpm, Error = 32.25 bpm\n",
      "16.mp4: Predicted = 71.64 bpm, Actual = 79.00 bpm, Error = 7.36 bpm\n",
      "17.mp4: Predicted = 84.32 bpm, Actual = 107.00 bpm, Error = 22.68 bpm\n",
      "18.mp4: Predicted = 66.60 bpm, Actual = 66.00 bpm, Error = 0.60 bpm\n",
      "19.mp4: Predicted = 105.69 bpm, Actual = 119.00 bpm, Error = 13.31 bpm\n",
      "2.mp4: Predicted = 88.18 bpm, Actual = 86.00 bpm, Error = 2.18 bpm\n",
      "20.mp4: Predicted = 60.61 bpm, Actual = 79.00 bpm, Error = 18.39 bpm\n",
      "21.mp4: Predicted = 111.64 bpm, Actual = 64.00 bpm, Error = 47.64 bpm\n",
      "22.mp4: Predicted = 60.85 bpm, Actual = 94.00 bpm, Error = 33.15 bpm\n",
      "23.mp4: Predicted = 71.79 bpm, Actual = 79.00 bpm, Error = 7.21 bpm\n",
      "24.mp4: Predicted = 65.15 bpm, Actual = 97.00 bpm, Error = 31.85 bpm\n",
      "25.mp4: Predicted = 81.42 bpm, Actual = 77.00 bpm, Error = 4.42 bpm\n",
      "26.mp4: Predicted = 72.33 bpm, Actual = 98.00 bpm, Error = 25.67 bpm\n",
      "27.mp4: Predicted = 60.37 bpm, Actual = 102.00 bpm, Error = 41.63 bpm\n",
      "28.mp4: Predicted = 62.15 bpm, Actual = 87.00 bpm, Error = 24.85 bpm\n",
      "29.mp4: Predicted = 98.35 bpm, Actual = 102.00 bpm, Error = 3.65 bpm\n",
      "3.mp4: Predicted = 71.64 bpm, Actual = 79.00 bpm, Error = 7.36 bpm\n",
      "30.mp4: Predicted = 56.99 bpm, Actual = 87.00 bpm, Error = 30.01 bpm\n",
      "31.mp4: Predicted = 76.57 bpm, Actual = 116.00 bpm, Error = 39.43 bpm\n",
      "32.mp4: Predicted = 72.39 bpm, Actual = 70.00 bpm, Error = 2.39 bpm\n",
      "33.mp4: Predicted = 72.64 bpm, Actual = 81.00 bpm, Error = 8.36 bpm\n",
      "34.mp4: Predicted = 66.30 bpm, Actual = 78.00 bpm, Error = 11.70 bpm\n",
      "35.mp4: Predicted = 68.73 bpm, Actual = 79.00 bpm, Error = 10.27 bpm\n",
      "36.mp4: Predicted = 71.56 bpm, Actual = 106.00 bpm, Error = 34.44 bpm\n",
      "37.mp4: Predicted = 58.68 bpm, Actual = 116.00 bpm, Error = 57.32 bpm\n",
      "38.mp4: Predicted = 103.40 bpm, Actual = 90.00 bpm, Error = 13.40 bpm\n",
      "39.mp4: Predicted = 72.07 bpm, Actual = 91.00 bpm, Error = 18.93 bpm\n",
      "4.mp4: Predicted = 84.32 bpm, Actual = 107.00 bpm, Error = 22.68 bpm\n",
      "40.mp4: Predicted = 79.48 bpm, Actual = 110.00 bpm, Error = 30.52 bpm\n",
      "41.mp4: Predicted = 121.40 bpm, Actual = 112.00 bpm, Error = 9.40 bpm\n",
      "42.mp4: Predicted = 87.79 bpm, Actual = 106.00 bpm, Error = 18.21 bpm\n",
      "43.mp4: Predicted = 85.09 bpm, Actual = 110.00 bpm, Error = 24.91 bpm\n",
      "44.mp4: Predicted = 59.78 bpm, Actual = 73.00 bpm, Error = 13.22 bpm\n",
      "45.mp4: Predicted = 95.43 bpm, Actual = 105.00 bpm, Error = 9.57 bpm\n",
      "46.mp4: Predicted = 97.45 bpm, Actual = 110.00 bpm, Error = 12.55 bpm\n",
      "47.mp4: Predicted = 124.40 bpm, Actual = 116.00 bpm, Error = 8.40 bpm\n",
      "48.mp4: Predicted = 62.70 bpm, Actual = 72.00 bpm, Error = 9.30 bpm\n",
      "49.mp4: Predicted = 94.67 bpm, Actual = 82.00 bpm, Error = 12.67 bpm\n",
      "5.mp4: Predicted = 66.60 bpm, Actual = 66.00 bpm, Error = 0.60 bpm\n",
      "50.mp4: Predicted = 67.37 bpm, Actual = 102.00 bpm, Error = 34.63 bpm\n",
      "51.mp4: Predicted = 54.59 bpm, Actual = 108.00 bpm, Error = 53.41 bpm\n",
      "6.mp4: Predicted = 105.69 bpm, Actual = 119.00 bpm, Error = 13.31 bpm\n",
      "7.mp4: Predicted = 60.61 bpm, Actual = 79.00 bpm, Error = 18.39 bpm\n",
      "8.mp4: Predicted = 70.20 bpm, Actual = 64.00 bpm, Error = 6.20 bpm\n",
      "9.mp4: Predicted = 49.47 bpm, Actual = 72.00 bpm, Error = 22.53 bpm\n",
      "hargun.mp4: Predicted = 79.00 bpm, Actual = 89.00 bpm, Error = 10.00 bpm\n",
      "mohit.mp4: Predicted = 91.72 bpm, Actual = 99.00 bpm, Error = 7.28 bpm\n",
      "shubm.mp4: Predicted = 69.81 bpm, Actual = 94.00 bpm, Error = 24.19 bpm\n",
      "vasu.mp4: Predicted = 88.47 bpm, Actual = 76.00 bpm, Error = 12.47 bpm\n",
      "\n",
      "Overall MAE across 55 videos: 19.42 bpm\n",
      "Average Error Percentage: 21.50%\n"
     ]
    }
   ],
   "source": [
    "# 9.3 SELECTION OF CNN WITH SELF ATTENTIONMECHANSISM (TRAINING + VALIDATION ON VIDEO DATASET) initially for 20 epochs\n",
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# ======== Video Dataset ============\n",
    "class VideoPPGDataset(Dataset):\n",
    "    def __init__(self, video_dir, label_dict, max_frames=300):\n",
    "        self.video_dir = video_dir\n",
    "        self.label_dict = label_dict\n",
    "        self.video_files = [f for f in os.listdir(video_dir) if f.endswith('.mp4') or f.endswith('.avi')]\n",
    "        self.max_frames = max_frames\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.video_files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file = self.video_files[idx]\n",
    "        filepath = os.path.join(self.video_dir, file)\n",
    "        cap = cv2.VideoCapture(filepath)\n",
    "\n",
    "        green_signal = []\n",
    "        x_motion = []\n",
    "\n",
    "        prev_frame = None\n",
    "        count = 0\n",
    "        while cap.isOpened() and count < self.max_frames:\n",
    "            ret, frame = cap.read()\n",
    "            if not ret:\n",
    "                break\n",
    "\n",
    "            roi = frame[100:200, 150:250]  # Adjust ROI\n",
    "            green = roi[:, :, 1].mean()\n",
    "            green_signal.append(green)\n",
    "    \n",
    "            if prev_frame is not None:\n",
    "                motion = np.abs(roi.astype(np.float32) - prev_frame.astype(np.float32)).mean()\n",
    "                x_motion.append(motion)\n",
    "            else:\n",
    "                x_motion.append(0.0)\n",
    "    \n",
    "            prev_frame = roi\n",
    "            count += 1\n",
    "    \n",
    "        cap.release()\n",
    "\n",
    "        # Convert to numpy and pad/truncate\n",
    "        green_signal = np.array(green_signal)\n",
    "        x_motion = np.array(x_motion)\n",
    "    \n",
    "        if len(green_signal) < self.max_frames:\n",
    "            pad_len = self.max_frames - len(green_signal)\n",
    "            green_signal = np.pad(green_signal, (0, pad_len), mode='edge')\n",
    "            x_motion = np.pad(x_motion, (0, pad_len), mode='edge')\n",
    "        else:\n",
    "            green_signal = green_signal[:self.max_frames]\n",
    "            x_motion = x_motion[:self.max_frames]\n",
    "\n",
    "        # Normalize\n",
    "        green_signal = (green_signal - np.mean(green_signal)) / (np.std(green_signal) + 1e-8)\n",
    "        x_motion = (x_motion - np.mean(x_motion)) / (np.std(x_motion) + 1e-8)\n",
    "    \n",
    "        sample = np.stack([green_signal, x_motion], axis=0)  # [2, max_frames]\n",
    "        label = float(self.label_dict.get(os.path.splitext(file)[0], 0))\n",
    "    \n",
    "        return torch.tensor(sample, dtype=torch.float32), torch.tensor(label, dtype=torch.float32), file\n",
    "\n",
    "\n",
    "# ======== Hybrid HR Estimation Model ============\n",
    "class ConvFeatureExtractor(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 16, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(16, 32, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.conv(x)\n",
    "\n",
    "\n",
    "class SelfAttentionBlock(nn.Module):\n",
    "    def __init__(self, in_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Linear(in_dim, in_dim)\n",
    "        self.key = nn.Linear(in_dim, in_dim)\n",
    "        self.value = nn.Linear(in_dim, in_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        q, k, v = self.query(x), self.key(x), self.value(x)\n",
    "        scores = torch.bmm(q, k.transpose(1, 2)) / (x.size(-1) ** 0.5)\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        return torch.bmm(attn_weights, v)\n",
    "\n",
    "\n",
    "# ======== CNN + Self-Attention Model ============\n",
    "class CNNBlock(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 32, kernel_size=5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):  # x: [B, 2, T]\n",
    "        return self.conv(x)  # [B, 128, T]\n",
    "\n",
    "\n",
    "class SelfAttention(nn.Module):\n",
    "    def __init__(self, embed_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.key = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.value = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):  # x: [B, C, T]\n",
    "        Q = self.query(x).permute(0, 2, 1)  # [B, T, C]\n",
    "        K = self.key(x)                     # [B, C, T]\n",
    "        V = self.value(x).permute(0, 2, 1)  # [B, T, C]\n",
    "\n",
    "        scores = torch.bmm(Q, K) / (x.size(1) ** 0.5)  # [B, T, T]\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        out = torch.bmm(attn_weights, V)  # [B, T, C]\n",
    "        return out.permute(0, 2, 1)  # [B, C, T]\n",
    "\n",
    "\n",
    "class HRPredictionModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = CNNBlock()\n",
    "        self.attn = SelfAttention(embed_dim=128)\n",
    "        self.global_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.cnn(x)        # [B, 128, T]\n",
    "        x = self.attn(x)       # [B, 128, T]\n",
    "        x = self.global_pool(x).squeeze(-1)  # [B, 128]\n",
    "        return self.fc(x).squeeze(1)         # [B]\n",
    "\n",
    "\n",
    "# ======== Train Model ============\n",
    "def train_model(model, train_loader, val_loader, device, epochs=20):\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    criterion = nn.L1Loss()\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        for x_batch, y_batch, _ in train_loader:\n",
    "            x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(x_batch)\n",
    "            loss = criterion(output, y_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        model.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for x_val, y_val, _ in val_loader:\n",
    "                x_val, y_val = x_val.to(device), y_val.to(device)\n",
    "                pred = model(x_val)\n",
    "                val_loss += criterion(pred, y_val).item()\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {total_loss/len(train_loader):.4f}, Val Loss: {val_loss/len(val_loader):.4f}\")\n",
    "\n",
    "\n",
    "# ======== Evaluate on All Videos ============\n",
    "def evaluate_all_videos(model, dataset, device):\n",
    "    model.eval()\n",
    "    errors = []\n",
    "    predictions = []\n",
    "    ground_truths = []\n",
    "    filenames = []\n",
    "\n",
    "    loader = DataLoader(dataset, batch_size=1, shuffle=False)\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets, file in loader:\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            outputs = model(inputs).cpu().numpy()\n",
    "            targets = targets.cpu().numpy()\n",
    "\n",
    "            predictions.append(outputs[0])\n",
    "            ground_truths.append(targets[0])\n",
    "            filenames.append(file[0])\n",
    "            errors.append(abs(outputs[0] - targets[0]))\n",
    "\n",
    "    mae = np.mean(errors)\n",
    "    error_percent = np.mean([abs(p - t) / t * 100 for p, t in zip(predictions, ground_truths)])\n",
    "\n",
    "    print(\"\\n===== Per-Video Heart Rate Prediction =====\")\n",
    "    for f, pred, true, err in zip(filenames, predictions, ground_truths, errors):\n",
    "        print(f\"{f}: Predicted = {pred:.2f} bpm, Actual = {true:.2f} bpm, Error = {err:.2f} bpm\")\n",
    "\n",
    "    print(f\"\\nOverall MAE across {len(errors)} videos: {mae:.2f} bpm\")\n",
    "    print(f\"Average Error Percentage: {error_percent:.2f}%\")\n",
    "\n",
    "\n",
    "# ======== Prepare Dataset ============\n",
    "def load_label_dict(excel_path):\n",
    "    df = pd.read_excel(excel_path)\n",
    "    return {\n",
    "        str(row['FileName']).strip(): float(row['Heart Rate (bpm)'])\n",
    "        for _, row in df.iterrows()\n",
    "    }\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    video_dir = \"C:/Users/hp/Desktop/DatasetVideoFiles/DatasetVideoFiles\"\n",
    "    label_file = \"C:/Users/hp/Desktop/DatasetVideoFiles/ProjectDatasetReadings.xlsx\"\n",
    "\n",
    "    label_dict = load_label_dict(label_file)\n",
    "    dataset = VideoPPGDataset(video_dir, label_dict)\n",
    "\n",
    "    train_size = int(0.8 * len(dataset))\n",
    "    val_size = len(dataset) - train_size\n",
    "    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)\n",
    "\n",
    "    model = HRPredictionModel()\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    train_model(model, train_loader, val_loader, device)\n",
    "\n",
    "    # Evaluate on all 51 videos\n",
    "    evaluate_all_videos(model, dataset, device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c603c9dc-56ab-4016-9d74-45285927a05a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50, Train Loss: 90.2667, Val Loss: 84.0524\n",
      "Epoch 2/50, Train Loss: 91.3387, Val Loss: 83.5277\n",
      "Epoch 3/50, Train Loss: 89.9515, Val Loss: 81.9978\n",
      "Epoch 4/50, Train Loss: 85.5244, Val Loss: 74.5696\n",
      "Epoch 5/50, Train Loss: 70.9318, Val Loss: 54.6711\n",
      "Epoch 6/50, Train Loss: 48.1323, Val Loss: 30.1307\n",
      "Epoch 7/50, Train Loss: 37.2308, Val Loss: 22.2709\n",
      "Epoch 8/50, Train Loss: 34.2653, Val Loss: 19.4324\n",
      "Epoch 9/50, Train Loss: 26.9141, Val Loss: 14.9555\n",
      "Epoch 10/50, Train Loss: 23.3491, Val Loss: 15.4393\n",
      "Epoch 11/50, Train Loss: 22.6475, Val Loss: 11.7241\n",
      "Epoch 12/50, Train Loss: 19.5510, Val Loss: 12.1973\n",
      "Epoch 13/50, Train Loss: 21.4140, Val Loss: 11.6268\n",
      "Epoch 14/50, Train Loss: 20.3848, Val Loss: 12.7300\n",
      "Epoch 15/50, Train Loss: 20.4819, Val Loss: 10.4794\n",
      "Epoch 16/50, Train Loss: 20.0678, Val Loss: 10.8227\n",
      "Epoch 17/50, Train Loss: 20.6302, Val Loss: 13.2751\n",
      "Epoch 18/50, Train Loss: 18.4703, Val Loss: 11.2675\n",
      "Epoch 19/50, Train Loss: 19.9553, Val Loss: 12.2480\n",
      "Epoch 20/50, Train Loss: 19.2446, Val Loss: 11.2511\n",
      "Epoch 21/50, Train Loss: 18.5228, Val Loss: 10.4581\n",
      "Epoch 22/50, Train Loss: 19.5115, Val Loss: 10.3286\n",
      "Epoch 23/50, Train Loss: 18.2390, Val Loss: 9.6096\n",
      "Epoch 24/50, Train Loss: 17.7712, Val Loss: 9.1981\n",
      "Epoch 25/50, Train Loss: 19.3999, Val Loss: 8.4986\n",
      "Epoch 26/50, Train Loss: 19.3497, Val Loss: 11.1771\n",
      "Epoch 27/50, Train Loss: 20.2760, Val Loss: 8.5711\n",
      "Epoch 28/50, Train Loss: 18.8926, Val Loss: 8.1338\n",
      "Epoch 29/50, Train Loss: 16.9837, Val Loss: 9.1219\n",
      "Epoch 30/50, Train Loss: 18.4344, Val Loss: 12.0125\n",
      "Epoch 31/50, Train Loss: 17.2674, Val Loss: 10.7286\n",
      "Epoch 32/50, Train Loss: 18.0274, Val Loss: 7.7214\n",
      "Epoch 33/50, Train Loss: 16.5361, Val Loss: 9.8391\n",
      "Epoch 34/50, Train Loss: 19.1225, Val Loss: 11.1064\n",
      "Epoch 35/50, Train Loss: 17.9860, Val Loss: 8.9097\n",
      "Epoch 36/50, Train Loss: 18.2582, Val Loss: 7.3416\n",
      "Epoch 37/50, Train Loss: 18.0590, Val Loss: 8.5997\n",
      "Epoch 38/50, Train Loss: 17.6076, Val Loss: 8.0654\n",
      "Epoch 39/50, Train Loss: 16.0752, Val Loss: 7.3782\n",
      "Epoch 40/50, Train Loss: 17.3615, Val Loss: 9.8568\n",
      "Epoch 41/50, Train Loss: 19.4379, Val Loss: 11.1116\n",
      "Epoch 42/50, Train Loss: 15.9105, Val Loss: 7.3692\n",
      "Epoch 43/50, Train Loss: 15.6520, Val Loss: 7.3561\n",
      "Epoch 44/50, Train Loss: 15.3601, Val Loss: 6.9406\n",
      "Epoch 45/50, Train Loss: 17.3987, Val Loss: 7.0999\n",
      "Epoch 46/50, Train Loss: 16.2705, Val Loss: 6.9540\n",
      "Epoch 47/50, Train Loss: 15.7235, Val Loss: 7.3408\n",
      "Epoch 48/50, Train Loss: 15.3310, Val Loss: 7.8621\n",
      "Epoch 49/50, Train Loss: 14.8970, Val Loss: 7.1836\n",
      "Epoch 50/50, Train Loss: 16.3412, Val Loss: 7.4162\n",
      "\n",
      "===== Per-Video Heart Rate Prediction =====\n",
      "1.mp4: Predicted = 69.71 bpm, Actual = 88.00 bpm, Error = 18.29 bpm\n",
      "10.mp4: Predicted = 85.17 bpm, Actual = 83.00 bpm, Error = 2.17 bpm\n",
      "11.mp4: Predicted = 75.11 bpm, Actual = 86.00 bpm, Error = 10.89 bpm\n",
      "12.mp4: Predicted = 58.65 bpm, Actual = 87.00 bpm, Error = 28.35 bpm\n",
      "13.mp4: Predicted = 75.25 bpm, Actual = 83.00 bpm, Error = 7.75 bpm\n",
      "14.mp4: Predicted = 79.39 bpm, Actual = 73.00 bpm, Error = 6.39 bpm\n",
      "15.mp4: Predicted = 82.73 bpm, Actual = 59.00 bpm, Error = 23.73 bpm\n",
      "16.mp4: Predicted = 80.44 bpm, Actual = 79.00 bpm, Error = 1.44 bpm\n",
      "17.mp4: Predicted = 100.07 bpm, Actual = 107.00 bpm, Error = 6.93 bpm\n",
      "18.mp4: Predicted = 71.44 bpm, Actual = 66.00 bpm, Error = 5.44 bpm\n",
      "19.mp4: Predicted = 100.38 bpm, Actual = 119.00 bpm, Error = 18.62 bpm\n",
      "2.mp4: Predicted = 98.41 bpm, Actual = 86.00 bpm, Error = 12.41 bpm\n",
      "20.mp4: Predicted = 68.49 bpm, Actual = 79.00 bpm, Error = 10.51 bpm\n",
      "21.mp4: Predicted = 127.04 bpm, Actual = 64.00 bpm, Error = 63.04 bpm\n",
      "22.mp4: Predicted = 96.15 bpm, Actual = 94.00 bpm, Error = 2.15 bpm\n",
      "23.mp4: Predicted = 80.73 bpm, Actual = 79.00 bpm, Error = 1.73 bpm\n",
      "24.mp4: Predicted = 78.09 bpm, Actual = 97.00 bpm, Error = 18.91 bpm\n",
      "25.mp4: Predicted = 103.00 bpm, Actual = 77.00 bpm, Error = 26.00 bpm\n",
      "26.mp4: Predicted = 106.56 bpm, Actual = 98.00 bpm, Error = 8.56 bpm\n",
      "27.mp4: Predicted = 69.13 bpm, Actual = 102.00 bpm, Error = 32.87 bpm\n",
      "28.mp4: Predicted = 88.97 bpm, Actual = 87.00 bpm, Error = 1.97 bpm\n",
      "29.mp4: Predicted = 103.35 bpm, Actual = 102.00 bpm, Error = 1.35 bpm\n",
      "3.mp4: Predicted = 80.44 bpm, Actual = 79.00 bpm, Error = 1.44 bpm\n",
      "30.mp4: Predicted = 98.25 bpm, Actual = 87.00 bpm, Error = 11.25 bpm\n",
      "31.mp4: Predicted = 99.75 bpm, Actual = 116.00 bpm, Error = 16.25 bpm\n",
      "32.mp4: Predicted = 94.58 bpm, Actual = 70.00 bpm, Error = 24.58 bpm\n",
      "33.mp4: Predicted = 77.27 bpm, Actual = 81.00 bpm, Error = 3.73 bpm\n",
      "34.mp4: Predicted = 79.99 bpm, Actual = 78.00 bpm, Error = 1.99 bpm\n",
      "35.mp4: Predicted = 78.59 bpm, Actual = 79.00 bpm, Error = 0.41 bpm\n",
      "36.mp4: Predicted = 73.99 bpm, Actual = 106.00 bpm, Error = 32.01 bpm\n",
      "37.mp4: Predicted = 64.81 bpm, Actual = 116.00 bpm, Error = 51.19 bpm\n",
      "38.mp4: Predicted = 119.32 bpm, Actual = 90.00 bpm, Error = 29.32 bpm\n",
      "39.mp4: Predicted = 93.09 bpm, Actual = 91.00 bpm, Error = 2.09 bpm\n",
      "4.mp4: Predicted = 100.07 bpm, Actual = 107.00 bpm, Error = 6.93 bpm\n",
      "40.mp4: Predicted = 85.65 bpm, Actual = 110.00 bpm, Error = 24.35 bpm\n",
      "41.mp4: Predicted = 114.73 bpm, Actual = 112.00 bpm, Error = 2.73 bpm\n",
      "42.mp4: Predicted = 112.24 bpm, Actual = 106.00 bpm, Error = 6.24 bpm\n",
      "43.mp4: Predicted = 103.62 bpm, Actual = 110.00 bpm, Error = 6.38 bpm\n",
      "44.mp4: Predicted = 75.41 bpm, Actual = 73.00 bpm, Error = 2.41 bpm\n",
      "45.mp4: Predicted = 92.91 bpm, Actual = 105.00 bpm, Error = 12.09 bpm\n",
      "46.mp4: Predicted = 105.64 bpm, Actual = 110.00 bpm, Error = 4.36 bpm\n",
      "47.mp4: Predicted = 119.95 bpm, Actual = 116.00 bpm, Error = 3.95 bpm\n",
      "48.mp4: Predicted = 80.93 bpm, Actual = 72.00 bpm, Error = 8.93 bpm\n",
      "49.mp4: Predicted = 101.11 bpm, Actual = 82.00 bpm, Error = 19.11 bpm\n",
      "5.mp4: Predicted = 71.44 bpm, Actual = 66.00 bpm, Error = 5.44 bpm\n",
      "50.mp4: Predicted = 61.80 bpm, Actual = 102.00 bpm, Error = 40.20 bpm\n",
      "51.mp4: Predicted = 60.83 bpm, Actual = 108.00 bpm, Error = 47.17 bpm\n",
      "6.mp4: Predicted = 100.38 bpm, Actual = 119.00 bpm, Error = 18.62 bpm\n",
      "7.mp4: Predicted = 68.49 bpm, Actual = 79.00 bpm, Error = 10.51 bpm\n",
      "8.mp4: Predicted = 89.30 bpm, Actual = 64.00 bpm, Error = 25.30 bpm\n",
      "9.mp4: Predicted = 53.66 bpm, Actual = 72.00 bpm, Error = 18.34 bpm\n",
      "mohit.mp4: Predicted = 102.28 bpm, Actual = 99.00 bpm, Error = 3.28 bpm\n",
      "shubham.mp4: Predicted = 101.83 bpm, Actual = 89.00 bpm, Error = 12.83 bpm\n",
      "vasu.mp4: Predicted = 74.25 bpm, Actual = 74.00 bpm, Error = 0.25 bpm\n",
      "\n",
      "Overall MAE across 54 videos: 14.13 bpm\n",
      "Average Error Percentage: 16.16%\n"
     ]
    }
   ],
   "source": [
    "# cnn with hyper parameter tuning (b. epochs=50, lr=0.005, etc.)\n",
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# ======== Video Dataset ============\n",
    "class VideoPPGDataset(Dataset):\n",
    "    def __init__(self, video_dir, label_dict, max_frames=600):  # Increased max frames\n",
    "        self.video_dir = video_dir\n",
    "        self.label_dict = label_dict\n",
    "        self.video_files = [f for f in os.listdir(video_dir) if f.endswith('.mp4') or f.endswith('.avi')]\n",
    "        self.max_frames = max_frames\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.video_files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file = self.video_files[idx]\n",
    "        filepath = os.path.join(self.video_dir, file)\n",
    "        cap = cv2.VideoCapture(filepath)\n",
    "\n",
    "        green_signal = []\n",
    "        x_motion = []\n",
    "\n",
    "        prev_frame = None\n",
    "        count = 0\n",
    "        while cap.isOpened() and count < self.max_frames:\n",
    "            ret, frame = cap.read()\n",
    "            if not ret:\n",
    "                break\n",
    "\n",
    "            roi = frame[100:200, 150:250]  # Adjust ROI\n",
    "            green = roi[:, :, 1].mean()\n",
    "            green_signal.append(green)\n",
    "    \n",
    "            if prev_frame is not None:\n",
    "                motion = np.abs(roi.astype(np.float32) - prev_frame.astype(np.float32)).mean()\n",
    "                x_motion.append(motion)\n",
    "            else:\n",
    "                x_motion.append(0.0)\n",
    "    \n",
    "            prev_frame = roi\n",
    "            count += 1\n",
    "    \n",
    "        cap.release()\n",
    "\n",
    "        # Convert to numpy and pad/truncate\n",
    "        green_signal = np.array(green_signal)\n",
    "        x_motion = np.array(x_motion)\n",
    "    \n",
    "        if len(green_signal) < self.max_frames:\n",
    "            pad_len = self.max_frames - len(green_signal)\n",
    "            green_signal = np.pad(green_signal, (0, pad_len), mode='edge')\n",
    "            x_motion = np.pad(x_motion, (0, pad_len), mode='edge')\n",
    "        else:\n",
    "            green_signal = green_signal[:self.max_frames]\n",
    "            x_motion = x_motion[:self.max_frames]\n",
    "\n",
    "        # Normalize\n",
    "        green_signal = (green_signal - np.mean(green_signal)) / (np.std(green_signal) + 1e-8)\n",
    "        x_motion = (x_motion - np.mean(x_motion)) / (np.std(x_motion) + 1e-8)\n",
    "    \n",
    "        sample = np.stack([green_signal, x_motion], axis=0)  # [2, max_frames]\n",
    "        label = float(self.label_dict.get(os.path.splitext(file)[0], 0))\n",
    "    \n",
    "        return torch.tensor(sample, dtype=torch.float32), torch.tensor(label, dtype=torch.float32), file\n",
    "\n",
    "\n",
    "# ======== Hybrid HR Estimation Model ============\n",
    "class ConvFeatureExtractor(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 16, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(16, 32, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.conv(x)\n",
    "\n",
    "\n",
    "class SelfAttentionBlock(nn.Module):\n",
    "    def __init__(self, in_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Linear(in_dim, in_dim)\n",
    "        self.key = nn.Linear(in_dim, in_dim)\n",
    "        self.value = nn.Linear(in_dim, in_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        q, k, v = self.query(x), self.key(x), self.value(x)\n",
    "        scores = torch.bmm(q, k.transpose(1, 2)) / (x.size(-1) ** 0.5)\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        return torch.bmm(attn_weights, v)\n",
    "\n",
    "\n",
    "# ======== CNN + Self-Attention Model ============\n",
    "class CNNBlock(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 32, kernel_size=5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):  # x: [B, 2, T]\n",
    "        return self.conv(x)  # [B, 128, T]\n",
    "\n",
    "\n",
    "class SelfAttention(nn.Module):\n",
    "    def __init__(self, embed_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.key = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.value = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):  # x: [B, C, T]\n",
    "        Q = self.query(x).permute(0, 2, 1)  # [B, T, C]\n",
    "        K = self.key(x)                     # [B, C, T]\n",
    "        V = self.value(x).permute(0, 2, 1)  # [B, T, C]\n",
    "\n",
    "        scores = torch.bmm(Q, K) / (x.size(1) ** 0.5)  # [B, T, T]\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        out = torch.bmm(attn_weights, V)  # [B, T, C]\n",
    "        return out.permute(0, 2, 1)  # [B, C, T]\n",
    "\n",
    "\n",
    "class HRPredictionModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = CNNBlock()\n",
    "        self.attn = SelfAttention(embed_dim=128)\n",
    "        self.global_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.cnn(x)        # [B, 128, T]\n",
    "        x = self.attn(x)       # [B, 128, T]\n",
    "        x = self.global_pool(x).squeeze(-1)  # [B, 128]\n",
    "        return self.fc(x).squeeze(1)         # [B]\n",
    "\n",
    "\n",
    "# ======== Train Model ============\n",
    "def train_model(model, train_loader, val_loader, device, epochs=50): \n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.0005)  \n",
    "    criterion = nn.HuberLoss()  # Changed to Huber Loss\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        for x_batch, y_batch, _ in train_loader:\n",
    "            x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(x_batch)\n",
    "            loss = criterion(output, y_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        model.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for x_val, y_val, _ in val_loader:\n",
    "                x_val, y_val = x_val.to(device), y_val.to(device)\n",
    "                pred = model(x_val)\n",
    "                val_loss += criterion(pred, y_val).item()\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {total_loss/len(train_loader):.4f}, Val Loss: {val_loss/len(val_loader):.4f}\")\n",
    "\n",
    "\n",
    "# ======== Evaluate on All Videos ============\n",
    "def evaluate_all_videos(model, dataset, device):\n",
    "    model.eval()\n",
    "    errors = []\n",
    "    predictions = []\n",
    "    ground_truths = []\n",
    "    filenames = []\n",
    "\n",
    "    loader = DataLoader(dataset, batch_size=1, shuffle=False)\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets, file in loader:\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            outputs = model(inputs).cpu().numpy()\n",
    "            targets = targets.cpu().numpy()\n",
    "\n",
    "            predictions.append(outputs[0])\n",
    "            ground_truths.append(targets[0])\n",
    "            filenames.append(file[0])\n",
    "            errors.append(abs(outputs[0] - targets[0]))\n",
    "\n",
    "    mae = np.mean(errors)\n",
    "    error_percent = np.mean([abs(p - t) / t * 100 for p, t in zip(predictions, ground_truths)])\n",
    "\n",
    "    print(\"\\n===== Per-Video Heart Rate Prediction =====\")\n",
    "    for f, pred, true, err in zip(filenames, predictions, ground_truths, errors):\n",
    "        print(f\"{f}: Predicted = {pred:.2f} bpm, Actual = {true:.2f} bpm, Error = {err:.2f} bpm\")\n",
    "\n",
    "    print(f\"\\nOverall MAE across {len(errors)} videos: {mae:.2f} bpm\")\n",
    "    print(f\"Average Error Percentage: {error_percent:.2f}%\")\n",
    "\n",
    "\n",
    "# ======== Prepare Dataset ============\n",
    "def load_label_dict(excel_path):\n",
    "    df = pd.read_excel(excel_path)\n",
    "    return {\n",
    "        str(row['FileName']).strip(): float(row['Heart Rate (bpm)'])\n",
    "        for _, row in df.iterrows()\n",
    "    }\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    video_dir = \"C:/Users/hp/Desktop/DatasetVideoFiles/DatasetVideoFiles\"\n",
    "    label_file = \"C:/Users/hp/Desktop/DatasetVideoFiles/ProjectDatasetReadings.xlsx\"\n",
    "\n",
    "    label_dict = load_label_dict(label_file)\n",
    "    dataset = VideoPPGDataset(video_dir, label_dict)\n",
    "\n",
    "    train_size = int(0.8 * len(dataset))\n",
    "    val_size = len(dataset) - train_size\n",
    "    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)  # Adjusted batch size\n",
    "    val_loader = DataLoader(val_dataset, batch_size=8, shuffle=False)  # Adjusted batch size\n",
    "\n",
    "    model = HRPredictionModel()\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    train_model(model, train_loader, val_loader, device)\n",
    "\n",
    "    # Evaluate on all 51 videos\n",
    "    evaluate_all_videos(model, dataset, device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d8026a4-0ecd-43a6-80ad-31691dbff987",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200, Train Loss: 87.8617, Val Loss: 84.7006\n",
      "Epoch 2/200, Train Loss: 53.0482, Val Loss: 35.0754\n",
      "Epoch 3/200, Train Loss: 37.0557, Val Loss: 36.7990\n",
      "Epoch 4/200, Train Loss: 28.9158, Val Loss: 26.1144\n",
      "Epoch 5/200, Train Loss: 20.4838, Val Loss: 23.5322\n",
      "Epoch 6/200, Train Loss: 19.5456, Val Loss: 41.5120\n",
      "Epoch 7/200, Train Loss: 21.1496, Val Loss: 19.4392\n",
      "Epoch 8/200, Train Loss: 18.6876, Val Loss: 24.5900\n",
      "Epoch 9/200, Train Loss: 19.2784, Val Loss: 24.3634\n",
      "Epoch 10/200, Train Loss: 15.9785, Val Loss: 26.6858\n",
      "Epoch 11/200, Train Loss: 15.7967, Val Loss: 23.4304\n",
      "Epoch 12/200, Train Loss: 15.8829, Val Loss: 22.1314\n",
      "Epoch 13/200, Train Loss: 14.7090, Val Loss: 26.4020\n",
      "Epoch 14/200, Train Loss: 14.7521, Val Loss: 22.7844\n",
      "Epoch 15/200, Train Loss: 14.8153, Val Loss: 22.3264\n",
      "Epoch 16/200, Train Loss: 14.7674, Val Loss: 18.7633\n",
      "Epoch 17/200, Train Loss: 14.8452, Val Loss: 18.5992\n",
      "Epoch 18/200, Train Loss: 14.7724, Val Loss: 29.0527\n",
      "Epoch 19/200, Train Loss: 14.5491, Val Loss: 17.5301\n",
      "Epoch 20/200, Train Loss: 15.4754, Val Loss: 30.4144\n",
      "Epoch 21/200, Train Loss: 15.6500, Val Loss: 22.9051\n",
      "Epoch 22/200, Train Loss: 16.5502, Val Loss: 18.9346\n",
      "Epoch 23/200, Train Loss: 17.0930, Val Loss: 32.0145\n",
      "Epoch 24/200, Train Loss: 13.0692, Val Loss: 16.6331\n",
      "Epoch 25/200, Train Loss: 13.2395, Val Loss: 18.7549\n",
      "Epoch 26/200, Train Loss: 13.1129, Val Loss: 22.5889\n",
      "Epoch 27/200, Train Loss: 12.7490, Val Loss: 23.5075\n",
      "Epoch 28/200, Train Loss: 12.1965, Val Loss: 15.9972\n",
      "Epoch 29/200, Train Loss: 12.4306, Val Loss: 19.4199\n",
      "Epoch 30/200, Train Loss: 11.9435, Val Loss: 22.8571\n",
      "Epoch 31/200, Train Loss: 12.7741, Val Loss: 18.3646\n",
      "Epoch 32/200, Train Loss: 11.5777, Val Loss: 21.0106\n",
      "Epoch 33/200, Train Loss: 11.8948, Val Loss: 22.0367\n",
      "Epoch 34/200, Train Loss: 13.0327, Val Loss: 17.9622\n",
      "Epoch 35/200, Train Loss: 12.3051, Val Loss: 22.9383\n",
      "Epoch 36/200, Train Loss: 13.1460, Val Loss: 26.8173\n",
      "Epoch 37/200, Train Loss: 13.1708, Val Loss: 24.3915\n",
      "Epoch 38/200, Train Loss: 11.4821, Val Loss: 27.1572\n",
      "Epoch 39/200, Train Loss: 13.3328, Val Loss: 19.8257\n",
      "Epoch 40/200, Train Loss: 15.3470, Val Loss: 23.1648\n",
      "Epoch 41/200, Train Loss: 13.5635, Val Loss: 26.1405\n",
      "Epoch 42/200, Train Loss: 12.5104, Val Loss: 19.5557\n",
      "Epoch 43/200, Train Loss: 11.6610, Val Loss: 21.9979\n",
      "Epoch 44/200, Train Loss: 12.5670, Val Loss: 16.0326\n",
      "Epoch 45/200, Train Loss: 12.9453, Val Loss: 18.2944\n",
      "Epoch 46/200, Train Loss: 13.2134, Val Loss: 28.8180\n",
      "Epoch 47/200, Train Loss: 13.2405, Val Loss: 17.3224\n",
      "Epoch 48/200, Train Loss: 10.8817, Val Loss: 16.7702\n",
      "Epoch 49/200, Train Loss: 12.8548, Val Loss: 17.4433\n",
      "Epoch 50/200, Train Loss: 11.1016, Val Loss: 36.2258\n",
      "Epoch 51/200, Train Loss: 15.1323, Val Loss: 17.4335\n",
      "Epoch 52/200, Train Loss: 10.2639, Val Loss: 26.1236\n",
      "Epoch 53/200, Train Loss: 10.8781, Val Loss: 17.8981\n",
      "Epoch 54/200, Train Loss: 11.2335, Val Loss: 31.4871\n",
      "Epoch 55/200, Train Loss: 11.6347, Val Loss: 15.3115\n",
      "Epoch 56/200, Train Loss: 10.9242, Val Loss: 20.5009\n",
      "Epoch 57/200, Train Loss: 9.6916, Val Loss: 15.8187\n",
      "Epoch 58/200, Train Loss: 9.6685, Val Loss: 23.2702\n",
      "Epoch 59/200, Train Loss: 10.8022, Val Loss: 16.3931\n",
      "Epoch 60/200, Train Loss: 10.2046, Val Loss: 16.6427\n",
      "Epoch 61/200, Train Loss: 10.2142, Val Loss: 25.1576\n",
      "Epoch 62/200, Train Loss: 10.1719, Val Loss: 20.7483\n",
      "Epoch 63/200, Train Loss: 11.6926, Val Loss: 25.6320\n",
      "Epoch 64/200, Train Loss: 10.4890, Val Loss: 28.3415\n",
      "Epoch 65/200, Train Loss: 10.9666, Val Loss: 21.0820\n",
      "Epoch 66/200, Train Loss: 9.3179, Val Loss: 20.0656\n",
      "Epoch 67/200, Train Loss: 8.6154, Val Loss: 20.3748\n",
      "Epoch 68/200, Train Loss: 8.6896, Val Loss: 28.3175\n",
      "Epoch 69/200, Train Loss: 8.6343, Val Loss: 17.9310\n",
      "Epoch 70/200, Train Loss: 9.4538, Val Loss: 27.2364\n",
      "Epoch 71/200, Train Loss: 9.9331, Val Loss: 25.2865\n",
      "Epoch 72/200, Train Loss: 10.4652, Val Loss: 19.5333\n",
      "Epoch 73/200, Train Loss: 10.1844, Val Loss: 22.4682\n",
      "Epoch 74/200, Train Loss: 9.3832, Val Loss: 29.5289\n",
      "Epoch 75/200, Train Loss: 10.6432, Val Loss: 19.6229\n",
      "Epoch 76/200, Train Loss: 9.3975, Val Loss: 24.2502\n",
      "Epoch 77/200, Train Loss: 9.8200, Val Loss: 20.2281\n",
      "Epoch 78/200, Train Loss: 9.6149, Val Loss: 20.5204\n",
      "Epoch 79/200, Train Loss: 8.9811, Val Loss: 20.7719\n",
      "Epoch 80/200, Train Loss: 8.4251, Val Loss: 23.1202\n",
      "Epoch 81/200, Train Loss: 8.6498, Val Loss: 19.7609\n",
      "Epoch 82/200, Train Loss: 8.5088, Val Loss: 21.1507\n",
      "Epoch 83/200, Train Loss: 7.6465, Val Loss: 21.8953\n",
      "Epoch 84/200, Train Loss: 7.6085, Val Loss: 19.1653\n",
      "Epoch 85/200, Train Loss: 7.3630, Val Loss: 19.6677\n",
      "Epoch 86/200, Train Loss: 7.8899, Val Loss: 19.4112\n",
      "Epoch 87/200, Train Loss: 8.4223, Val Loss: 18.1378\n",
      "Epoch 88/200, Train Loss: 11.4034, Val Loss: 18.5135\n",
      "Epoch 89/200, Train Loss: 7.4680, Val Loss: 19.0692\n",
      "Epoch 90/200, Train Loss: 7.1834, Val Loss: 21.3696\n",
      "Epoch 91/200, Train Loss: 6.6795, Val Loss: 22.0367\n",
      "Epoch 92/200, Train Loss: 8.4703, Val Loss: 25.9893\n",
      "Epoch 93/200, Train Loss: 8.1126, Val Loss: 23.6316\n",
      "Epoch 94/200, Train Loss: 7.4649, Val Loss: 21.0309\n",
      "Epoch 95/200, Train Loss: 7.3697, Val Loss: 20.1416\n",
      "Epoch 96/200, Train Loss: 7.6840, Val Loss: 20.5578\n",
      "Epoch 97/200, Train Loss: 5.7267, Val Loss: 19.9425\n",
      "Epoch 98/200, Train Loss: 5.6452, Val Loss: 20.6020\n",
      "Epoch 99/200, Train Loss: 6.9560, Val Loss: 20.6828\n",
      "Epoch 100/200, Train Loss: 9.6652, Val Loss: 20.7891\n",
      "Epoch 101/200, Train Loss: 8.5729, Val Loss: 26.7785\n",
      "Epoch 102/200, Train Loss: 9.4005, Val Loss: 37.6114\n",
      "Epoch 103/200, Train Loss: 21.7411, Val Loss: 18.7583\n",
      "Epoch 104/200, Train Loss: 18.5441, Val Loss: 24.6898\n",
      "Epoch 105/200, Train Loss: 15.1775, Val Loss: 19.7263\n",
      "Epoch 106/200, Train Loss: 12.1700, Val Loss: 24.2292\n",
      "Epoch 107/200, Train Loss: 10.7892, Val Loss: 20.9164\n",
      "Epoch 108/200, Train Loss: 9.6296, Val Loss: 26.1333\n",
      "Epoch 109/200, Train Loss: 9.2504, Val Loss: 23.4545\n",
      "Epoch 110/200, Train Loss: 8.9804, Val Loss: 21.1286\n",
      "Epoch 111/200, Train Loss: 7.6804, Val Loss: 23.8149\n",
      "Epoch 112/200, Train Loss: 7.5280, Val Loss: 20.2313\n",
      "Epoch 113/200, Train Loss: 7.7463, Val Loss: 20.5770\n",
      "Epoch 114/200, Train Loss: 7.6455, Val Loss: 22.4598\n",
      "Epoch 115/200, Train Loss: 7.2563, Val Loss: 24.3275\n",
      "Epoch 116/200, Train Loss: 6.9728, Val Loss: 22.7427\n",
      "Epoch 117/200, Train Loss: 8.6385, Val Loss: 19.0163\n",
      "Epoch 118/200, Train Loss: 7.0053, Val Loss: 22.6634\n",
      "Epoch 119/200, Train Loss: 7.8221, Val Loss: 20.4700\n",
      "Epoch 120/200, Train Loss: 7.8191, Val Loss: 19.3443\n",
      "Epoch 121/200, Train Loss: 7.9232, Val Loss: 20.2446\n",
      "Epoch 122/200, Train Loss: 6.9337, Val Loss: 19.7880\n",
      "Epoch 123/200, Train Loss: 7.6046, Val Loss: 21.0371\n",
      "Epoch 124/200, Train Loss: 7.5234, Val Loss: 22.6733\n",
      "Epoch 125/200, Train Loss: 6.3730, Val Loss: 22.3751\n",
      "Epoch 126/200, Train Loss: 6.2129, Val Loss: 22.3527\n",
      "Epoch 127/200, Train Loss: 6.4244, Val Loss: 20.9166\n",
      "Epoch 128/200, Train Loss: 6.1486, Val Loss: 23.1319\n",
      "Epoch 129/200, Train Loss: 7.2156, Val Loss: 28.6953\n",
      "Epoch 130/200, Train Loss: 9.0955, Val Loss: 19.6209\n",
      "Epoch 131/200, Train Loss: 7.2849, Val Loss: 21.1023\n",
      "Epoch 132/200, Train Loss: 6.2639, Val Loss: 20.3339\n",
      "Epoch 133/200, Train Loss: 6.2990, Val Loss: 21.5849\n",
      "Epoch 134/200, Train Loss: 6.0449, Val Loss: 19.9185\n",
      "Epoch 135/200, Train Loss: 5.4775, Val Loss: 21.9357\n",
      "Epoch 136/200, Train Loss: 4.7975, Val Loss: 21.2072\n",
      "Epoch 137/200, Train Loss: 5.0612, Val Loss: 21.7806\n",
      "Epoch 138/200, Train Loss: 5.1942, Val Loss: 20.8269\n",
      "Epoch 139/200, Train Loss: 5.7681, Val Loss: 22.0889\n",
      "Epoch 140/200, Train Loss: 5.7366, Val Loss: 22.9002\n",
      "Epoch 141/200, Train Loss: 5.8895, Val Loss: 22.3486\n",
      "Epoch 142/200, Train Loss: 5.3777, Val Loss: 22.3821\n",
      "Epoch 143/200, Train Loss: 4.8974, Val Loss: 20.5061\n",
      "Epoch 144/200, Train Loss: 5.0121, Val Loss: 18.2792\n",
      "Epoch 145/200, Train Loss: 4.6920, Val Loss: 19.6097\n",
      "Epoch 146/200, Train Loss: 5.6836, Val Loss: 20.8622\n",
      "Epoch 147/200, Train Loss: 4.4885, Val Loss: 17.5114\n",
      "Epoch 148/200, Train Loss: 4.3620, Val Loss: 17.0187\n",
      "Epoch 149/200, Train Loss: 5.4593, Val Loss: 16.4687\n",
      "Epoch 150/200, Train Loss: 6.9102, Val Loss: 16.2003\n",
      "Epoch 151/200, Train Loss: 5.5094, Val Loss: 17.5174\n",
      "Epoch 152/200, Train Loss: 5.1116, Val Loss: 17.6558\n",
      "Epoch 153/200, Train Loss: 4.2271, Val Loss: 16.4841\n",
      "Epoch 154/200, Train Loss: 5.7811, Val Loss: 16.6537\n",
      "Epoch 155/200, Train Loss: 6.7322, Val Loss: 16.3553\n",
      "Epoch 156/200, Train Loss: 4.7702, Val Loss: 16.2597\n",
      "Epoch 157/200, Train Loss: 4.9461, Val Loss: 14.8182\n",
      "Epoch 158/200, Train Loss: 4.7046, Val Loss: 15.1374\n",
      "Epoch 159/200, Train Loss: 5.3525, Val Loss: 16.5288\n",
      "Epoch 160/200, Train Loss: 4.4415, Val Loss: 18.8248\n",
      "Epoch 161/200, Train Loss: 4.4254, Val Loss: 17.8937\n",
      "Epoch 162/200, Train Loss: 4.0695, Val Loss: 19.3627\n",
      "Epoch 163/200, Train Loss: 4.0310, Val Loss: 19.2431\n",
      "Epoch 164/200, Train Loss: 4.5048, Val Loss: 17.2068\n",
      "Epoch 165/200, Train Loss: 4.7598, Val Loss: 16.5753\n",
      "Epoch 166/200, Train Loss: 4.6907, Val Loss: 15.4853\n",
      "Epoch 167/200, Train Loss: 3.7151, Val Loss: 15.4106\n",
      "Epoch 168/200, Train Loss: 4.9448, Val Loss: 16.7952\n",
      "Epoch 169/200, Train Loss: 5.0796, Val Loss: 16.6189\n",
      "Epoch 170/200, Train Loss: 3.8754, Val Loss: 18.9297\n",
      "Epoch 171/200, Train Loss: 3.4072, Val Loss: 15.6218\n",
      "Epoch 172/200, Train Loss: 4.4671, Val Loss: 18.1849\n",
      "Epoch 173/200, Train Loss: 5.1902, Val Loss: 17.2495\n",
      "Epoch 174/200, Train Loss: 3.5768, Val Loss: 16.0100\n",
      "Epoch 175/200, Train Loss: 3.9596, Val Loss: 17.3775\n",
      "Epoch 176/200, Train Loss: 3.7136, Val Loss: 16.6686\n",
      "Epoch 177/200, Train Loss: 5.3128, Val Loss: 15.7129\n",
      "Epoch 178/200, Train Loss: 4.7565, Val Loss: 15.3690\n",
      "Epoch 179/200, Train Loss: 4.3480, Val Loss: 15.8015\n",
      "Epoch 180/200, Train Loss: 4.1491, Val Loss: 19.5608\n",
      "Epoch 181/200, Train Loss: 5.1235, Val Loss: 15.8048\n",
      "Epoch 182/200, Train Loss: 4.0146, Val Loss: 14.8339\n",
      "Epoch 183/200, Train Loss: 4.5228, Val Loss: 16.9046\n",
      "Epoch 184/200, Train Loss: 3.4105, Val Loss: 15.8286\n",
      "Epoch 185/200, Train Loss: 3.2862, Val Loss: 14.1289\n",
      "Epoch 186/200, Train Loss: 4.8579, Val Loss: 14.5150\n",
      "Epoch 187/200, Train Loss: 3.5714, Val Loss: 14.8622\n",
      "Epoch 188/200, Train Loss: 3.6841, Val Loss: 15.3733\n",
      "Epoch 189/200, Train Loss: 5.2919, Val Loss: 14.6390\n",
      "Epoch 190/200, Train Loss: 4.1833, Val Loss: 18.6913\n",
      "Epoch 191/200, Train Loss: 4.2260, Val Loss: 19.3260\n",
      "Epoch 192/200, Train Loss: 4.2185, Val Loss: 18.5230\n",
      "Epoch 193/200, Train Loss: 4.7766, Val Loss: 16.1804\n",
      "Epoch 194/200, Train Loss: 6.7574, Val Loss: 17.5747\n",
      "Epoch 195/200, Train Loss: 5.1072, Val Loss: 15.6982\n",
      "Epoch 196/200, Train Loss: 3.9853, Val Loss: 15.7965\n",
      "Epoch 197/200, Train Loss: 4.1830, Val Loss: 17.4360\n",
      "Epoch 198/200, Train Loss: 3.8896, Val Loss: 16.7004\n",
      "Epoch 199/200, Train Loss: 3.8757, Val Loss: 16.8486\n",
      "Epoch 200/200, Train Loss: 4.4838, Val Loss: 16.9737\n",
      "\n",
      "===== Per-Video Heart Rate Prediction =====\n",
      "1.mp4: Predicted = 134.85 bpm, Actual = 88.00 bpm, Error = 46.85 bpm\n",
      "10.mp4: Predicted = 86.32 bpm, Actual = 83.00 bpm, Error = 3.32 bpm\n",
      "11.mp4: Predicted = 88.80 bpm, Actual = 86.00 bpm, Error = 2.80 bpm\n",
      "12.mp4: Predicted = 87.77 bpm, Actual = 87.00 bpm, Error = 0.77 bpm\n",
      "13.mp4: Predicted = 85.82 bpm, Actual = 83.00 bpm, Error = 2.82 bpm\n",
      "14.mp4: Predicted = 96.40 bpm, Actual = 73.00 bpm, Error = 23.40 bpm\n",
      "15.mp4: Predicted = 61.75 bpm, Actual = 59.00 bpm, Error = 2.75 bpm\n",
      "16.mp4: Predicted = 79.77 bpm, Actual = 79.00 bpm, Error = 0.77 bpm\n",
      "17.mp4: Predicted = 107.46 bpm, Actual = 107.00 bpm, Error = 0.46 bpm\n",
      "18.mp4: Predicted = 73.21 bpm, Actual = 66.00 bpm, Error = 7.21 bpm\n",
      "19.mp4: Predicted = 125.38 bpm, Actual = 119.00 bpm, Error = 6.38 bpm\n",
      "2.mp4: Predicted = 100.73 bpm, Actual = 86.00 bpm, Error = 14.73 bpm\n",
      "20.mp4: Predicted = 83.46 bpm, Actual = 79.00 bpm, Error = 4.46 bpm\n",
      "21.mp4: Predicted = 128.44 bpm, Actual = 64.00 bpm, Error = 64.44 bpm\n",
      "22.mp4: Predicted = 97.45 bpm, Actual = 94.00 bpm, Error = 3.45 bpm\n",
      "23.mp4: Predicted = 80.49 bpm, Actual = 79.00 bpm, Error = 1.49 bpm\n",
      "24.mp4: Predicted = 95.96 bpm, Actual = 97.00 bpm, Error = 1.04 bpm\n",
      "25.mp4: Predicted = 79.37 bpm, Actual = 77.00 bpm, Error = 2.37 bpm\n",
      "26.mp4: Predicted = 108.89 bpm, Actual = 98.00 bpm, Error = 10.89 bpm\n",
      "27.mp4: Predicted = 106.44 bpm, Actual = 102.00 bpm, Error = 4.44 bpm\n",
      "28.mp4: Predicted = 87.44 bpm, Actual = 87.00 bpm, Error = 0.44 bpm\n",
      "29.mp4: Predicted = 104.07 bpm, Actual = 102.00 bpm, Error = 2.07 bpm\n",
      "3.mp4: Predicted = 79.77 bpm, Actual = 79.00 bpm, Error = 0.77 bpm\n",
      "30.mp4: Predicted = 73.46 bpm, Actual = 87.00 bpm, Error = 13.54 bpm\n",
      "31.mp4: Predicted = 119.29 bpm, Actual = 116.00 bpm, Error = 3.29 bpm\n",
      "32.mp4: Predicted = 72.48 bpm, Actual = 70.00 bpm, Error = 2.48 bpm\n",
      "33.mp4: Predicted = 88.99 bpm, Actual = 81.00 bpm, Error = 7.99 bpm\n",
      "34.mp4: Predicted = 84.47 bpm, Actual = 78.00 bpm, Error = 6.47 bpm\n",
      "35.mp4: Predicted = 83.59 bpm, Actual = 79.00 bpm, Error = 4.59 bpm\n",
      "36.mp4: Predicted = 108.85 bpm, Actual = 106.00 bpm, Error = 2.85 bpm\n",
      "37.mp4: Predicted = 122.22 bpm, Actual = 116.00 bpm, Error = 6.22 bpm\n",
      "38.mp4: Predicted = 103.73 bpm, Actual = 90.00 bpm, Error = 13.73 bpm\n",
      "39.mp4: Predicted = 93.03 bpm, Actual = 91.00 bpm, Error = 2.03 bpm\n",
      "4.mp4: Predicted = 107.46 bpm, Actual = 107.00 bpm, Error = 0.46 bpm\n",
      "40.mp4: Predicted = 104.48 bpm, Actual = 110.00 bpm, Error = 5.52 bpm\n",
      "41.mp4: Predicted = 112.31 bpm, Actual = 112.00 bpm, Error = 0.31 bpm\n",
      "42.mp4: Predicted = 124.43 bpm, Actual = 106.00 bpm, Error = 18.43 bpm\n",
      "43.mp4: Predicted = 116.92 bpm, Actual = 110.00 bpm, Error = 6.92 bpm\n",
      "44.mp4: Predicted = 76.26 bpm, Actual = 73.00 bpm, Error = 3.26 bpm\n",
      "45.mp4: Predicted = 103.55 bpm, Actual = 105.00 bpm, Error = 1.45 bpm\n",
      "46.mp4: Predicted = 117.18 bpm, Actual = 110.00 bpm, Error = 7.18 bpm\n",
      "47.mp4: Predicted = 148.91 bpm, Actual = 116.00 bpm, Error = 32.91 bpm\n",
      "48.mp4: Predicted = 78.95 bpm, Actual = 72.00 bpm, Error = 6.95 bpm\n",
      "49.mp4: Predicted = 86.69 bpm, Actual = 82.00 bpm, Error = 4.69 bpm\n",
      "5.mp4: Predicted = 73.21 bpm, Actual = 66.00 bpm, Error = 7.21 bpm\n",
      "50.mp4: Predicted = 104.92 bpm, Actual = 102.00 bpm, Error = 2.92 bpm\n",
      "51.mp4: Predicted = 112.19 bpm, Actual = 108.00 bpm, Error = 4.19 bpm\n",
      "6.mp4: Predicted = 125.38 bpm, Actual = 119.00 bpm, Error = 6.38 bpm\n",
      "7.mp4: Predicted = 83.46 bpm, Actual = 79.00 bpm, Error = 4.46 bpm\n",
      "8.mp4: Predicted = 67.96 bpm, Actual = 64.00 bpm, Error = 3.96 bpm\n",
      "9.mp4: Predicted = 82.95 bpm, Actual = 72.00 bpm, Error = 10.95 bpm\n",
      "mohit.mp4: Predicted = 99.38 bpm, Actual = 99.00 bpm, Error = 0.38 bpm\n",
      "sample1.mp4: Predicted = 77.25 bpm, Actual = 62.30 bpm, Error = 14.95 bpm\n",
      "sample10.mp4: Predicted = 93.02 bpm, Actual = 134.60 bpm, Error = 41.58 bpm\n",
      "sample2.mp4: Predicted = 121.06 bpm, Actual = 115.11 bpm, Error = 5.95 bpm\n",
      "sample3.mp4: Predicted = 104.24 bpm, Actual = 101.36 bpm, Error = 2.88 bpm\n",
      "sample4.mp4: Predicted = 70.06 bpm, Actual = 69.50 bpm, Error = 0.56 bpm\n",
      "sample5.mp4: Predicted = 99.57 bpm, Actual = 94.80 bpm, Error = 4.77 bpm\n",
      "sample6.mp4: Predicted = 86.88 bpm, Actual = 78.30 bpm, Error = 8.58 bpm\n",
      "sample7.mp4: Predicted = 96.41 bpm, Actual = 93.20 bpm, Error = 3.21 bpm\n",
      "sample8.mp4: Predicted = 92.93 bpm, Actual = 87.70 bpm, Error = 5.23 bpm\n",
      "sample9.mp4: Predicted = 77.57 bpm, Actual = 69.28 bpm, Error = 8.29 bpm\n",
      "shubham.mp4: Predicted = 93.70 bpm, Actual = 89.00 bpm, Error = 4.70 bpm\n",
      "vasu.mp4: Predicted = 83.80 bpm, Actual = 74.00 bpm, Error = 9.80 bpm\n",
      "\n",
      "Overall MAE across 64 videos: 7.97 bpm\n",
      "Average Error Percentage: 9.35%\n"
     ]
    }
   ],
   "source": [
    "# cnn with hyper parameter tuning best accuracy achieved since past trainings (c. epochs=200, lr=0.001, etc.)\n",
    "import os\n",
    "import cv2\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import transforms\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "# ======== Video Dataset ============\n",
    "class VideoPPGDataset(Dataset):\n",
    "    def __init__(self, video_dir, label_dict, max_frames=600):  # Increased max frames\n",
    "        self.video_dir = video_dir\n",
    "        self.label_dict = label_dict\n",
    "        self.video_files = [f for f in os.listdir(video_dir) if f.endswith('.mp4') or f.endswith('.avi')]\n",
    "        self.max_frames = max_frames\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.video_files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file = self.video_files[idx]\n",
    "        filepath = os.path.join(self.video_dir, file)\n",
    "        cap = cv2.VideoCapture(filepath)\n",
    "\n",
    "        green_signal = []\n",
    "        x_motion = []\n",
    "\n",
    "        prev_frame = None\n",
    "        count = 0\n",
    "        while cap.isOpened() and count < self.max_frames:\n",
    "            ret, frame = cap.read()\n",
    "            if not ret:\n",
    "                break\n",
    "\n",
    "            roi = frame[100:200, 150:250]  # Adjust ROI\n",
    "            green = roi[:, :, 1].mean()\n",
    "            green_signal.append(green)\n",
    "    \n",
    "            if prev_frame is not None:\n",
    "                motion = np.abs(roi.astype(np.float32) - prev_frame.astype(np.float32)).mean()\n",
    "                x_motion.append(motion)\n",
    "            else:\n",
    "                x_motion.append(0.0)\n",
    "    \n",
    "            prev_frame = roi\n",
    "            count += 1\n",
    "    \n",
    "        cap.release()\n",
    "\n",
    "        # Convert to numpy and pad/truncate\n",
    "        green_signal = np.array(green_signal)\n",
    "        x_motion = np.array(x_motion)\n",
    "    \n",
    "        if len(green_signal) < self.max_frames:\n",
    "            pad_len = self.max_frames - len(green_signal)\n",
    "            green_signal = np.pad(green_signal, (0, pad_len), mode='edge')\n",
    "            x_motion = np.pad(x_motion, (0, pad_len), mode='edge')\n",
    "        else:\n",
    "            green_signal = green_signal[:self.max_frames]\n",
    "            x_motion = x_motion[:self.max_frames]\n",
    "\n",
    "        # Normalize\n",
    "        green_signal = (green_signal - np.mean(green_signal)) / (np.std(green_signal) + 1e-8)\n",
    "        x_motion = (x_motion - np.mean(x_motion)) / (np.std(x_motion) + 1e-8)\n",
    "    \n",
    "        sample = np.stack([green_signal, x_motion], axis=0)  # [2, max_frames]\n",
    "        label = float(self.label_dict.get(os.path.splitext(file)[0], 0))\n",
    "    \n",
    "        return torch.tensor(sample, dtype=torch.float32), torch.tensor(label, dtype=torch.float32), file\n",
    "\n",
    "\n",
    "# ======== Hybrid HR Estimation Model ============\n",
    "class ConvFeatureExtractor(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 16, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(16, 32, kernel_size=5, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.conv(x)\n",
    "\n",
    "\n",
    "class SelfAttentionBlock(nn.Module):\n",
    "    def __init__(self, in_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Linear(in_dim, in_dim)\n",
    "        self.key = nn.Linear(in_dim, in_dim)\n",
    "        self.value = nn.Linear(in_dim, in_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        q, k, v = self.query(x), self.key(x), self.value(x)\n",
    "        scores = torch.bmm(q, k.transpose(1, 2)) / (x.size(-1) ** 0.5)\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        return torch.bmm(attn_weights, v)\n",
    "\n",
    "\n",
    "# ======== CNN + Self-Attention Model ============\n",
    "class CNNBlock(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = nn.Sequential(\n",
    "            nn.Conv1d(2, 32, kernel_size=5, stride=1, padding=2),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):  # x: [B, 2, T]\n",
    "        return self.conv(x)  # [B, 128, T]\n",
    "\n",
    "\n",
    "class SelfAttention(nn.Module):\n",
    "    def __init__(self, embed_dim):\n",
    "        super().__init__()\n",
    "        self.query = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.key = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.value = nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):  # x: [B, C, T]\n",
    "        Q = self.query(x).permute(0, 2, 1)  # [B, T, C]\n",
    "        K = self.key(x)                     # [B, C, T]\n",
    "        V = self.value(x).permute(0, 2, 1)  # [B, T, C]\n",
    "\n",
    "        scores = torch.bmm(Q, K) / (x.size(1) ** 0.5)  # [B, T, T]\n",
    "        attn_weights = F.softmax(scores, dim=-1)\n",
    "        out = torch.bmm(attn_weights, V)  # [B, T, C]\n",
    "        return out.permute(0, 2, 1)  # [B, C, T]\n",
    "\n",
    "\n",
    "class HRPredictionModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = CNNBlock()\n",
    "        self.attn = SelfAttention(embed_dim=128)\n",
    "        self.global_pool = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.cnn(x)        # [B, 128, T]\n",
    "        x = self.attn(x)       # [B, 128, T]\n",
    "        x = self.global_pool(x).squeeze(-1)  # [B, 128]\n",
    "        return self.fc(x).squeeze(1)         # [B]\n",
    "\n",
    "\n",
    "# ======== Train Model ============\n",
    "def train_model(model, train_loader, val_loader, device, epochs=200): \n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)  \n",
    "    criterion = nn.HuberLoss()  # Changed to Huber Loss\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        total_loss = 0\n",
    "        for x_batch, y_batch, _ in train_loader:\n",
    "            x_batch, y_batch = x_batch.to(device), y_batch.to(device)\n",
    "            optimizer.zero_grad()\n",
    "            output = model(x_batch)\n",
    "            loss = criterion(output, y_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            total_loss += loss.item()\n",
    "\n",
    "        model.eval()\n",
    "        val_loss = 0\n",
    "        with torch.no_grad():\n",
    "            for x_val, y_val, _ in val_loader:\n",
    "                x_val, y_val = x_val.to(device), y_val.to(device)\n",
    "                pred = model(x_val)\n",
    "                val_loss += criterion(pred, y_val).item()\n",
    "\n",
    "        print(f\"Epoch {epoch+1}/{epochs}, Train Loss: {total_loss/len(train_loader):.4f}, Val Loss: {val_loss/len(val_loader):.4f}\")\n",
    "\n",
    "\n",
    "# ======== Evaluate on All Videos ============\n",
    "def evaluate_all_videos(model, dataset, device):\n",
    "    model.eval()\n",
    "    errors = []\n",
    "    predictions = []\n",
    "    ground_truths = []\n",
    "    filenames = []\n",
    "\n",
    "    loader = DataLoader(dataset, batch_size=1, shuffle=False)\n",
    "    with torch.no_grad():\n",
    "        for inputs, targets, file in loader:\n",
    "            inputs, targets = inputs.to(device), targets.to(device)\n",
    "            outputs = model(inputs).cpu().numpy()\n",
    "            targets = targets.cpu().numpy()\n",
    "\n",
    "            predictions.append(outputs[0])\n",
    "            ground_truths.append(targets[0])\n",
    "            filenames.append(file[0])\n",
    "            errors.append(abs(outputs[0] - targets[0]))\n",
    "\n",
    "    mae = np.mean(errors)\n",
    "    error_percent = np.mean([abs(p - t) / t * 100 for p, t in zip(predictions, ground_truths)])\n",
    "\n",
    "    print(\"\\n===== Per-Video Heart Rate Prediction =====\")\n",
    "    for f, pred, true, err in zip(filenames, predictions, ground_truths, errors):\n",
    "        print(f\"{f}: Predicted = {pred:.2f} bpm, Actual = {true:.2f} bpm, Error = {err:.2f} bpm\")\n",
    "\n",
    "    print(f\"\\nOverall MAE across {len(errors)} videos: {mae:.2f} bpm\")\n",
    "    print(f\"Average Error Percentage: {error_percent:.2f}%\")\n",
    "\n",
    "\n",
    "# ======== Prepare Dataset ============\n",
    "def load_label_dict(excel_path):\n",
    "    df = pd.read_excel(excel_path)\n",
    "    return {\n",
    "        str(row['FileName']).strip(): float(row['Heart Rate (bpm)'])\n",
    "        for _, row in df.iterrows()\n",
    "    }\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    video_dir = \"C:/Users/hp/Desktop/DatasetVideoFiles/DatasetVideoFiles\"\n",
    "    label_file = \"C:/Users/hp/Desktop/DatasetVideoFiles/ProjectDatasetReadings.xlsx\"\n",
    "\n",
    "    label_dict = load_label_dict(label_file)\n",
    "    dataset = VideoPPGDataset(video_dir, label_dict)\n",
    "\n",
    "    train_size = int(0.8 * len(dataset))\n",
    "    val_size = len(dataset) - train_size\n",
    "    train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size])\n",
    "\n",
    "    train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)  # Adjusted batch size\n",
    "    val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)  # Adjusted batch size\n",
    "\n",
    "    model = HRPredictionModel()\n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    model.to(device)\n",
    "\n",
    "    train_model(model, train_loader, val_loader, device)\n",
    "\n",
    "    # Evaluate on all 51 videos\n",
    "    evaluate_all_videos(model, dataset, device)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e149d56-c88b-481f-b4f3-38d2dad3b01f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 10. saving the weights and basis for reuse.\n",
    "torch.save(model.state_dict(), \"hr_model_self_attention_best.pth\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b967e4f8-cd61-466b-bf9a-d339eb0c4bca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10.1: loading the final saved model for reused for single sample prediction \n",
    "model.load_state_dict(torch.load(\"hr_model_self_attention_best.pth\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "64e4df21-85c2-413a-9a5b-0c4efeb61a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 11. GUI simulated for single sample prediction\n",
    "import tkinter as tk\n",
    "from tkinter import filedialog, messagebox, ttk\n",
    "import torch\n",
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "# Model Definitions (same as yours)\n",
    "class CNNBlock(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.conv = torch.nn.Sequential(\n",
    "            torch.nn.Conv1d(2, 32, kernel_size=5, stride=1, padding=2),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv1d(32, 64, kernel_size=3, stride=1, padding=1),\n",
    "            torch.nn.ReLU(),\n",
    "            torch.nn.Conv1d(64, 128, kernel_size=3, stride=1, padding=1),\n",
    "            torch.nn.ReLU()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self.conv(x)\n",
    "\n",
    "class SelfAttention(torch.nn.Module):\n",
    "    def __init__(self, embed_dim):\n",
    "        super().__init__()\n",
    "        self.query = torch.nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.key = torch.nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "        self.value = torch.nn.Conv1d(embed_dim, embed_dim, kernel_size=1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        Q = self.query(x).permute(0, 2, 1)\n",
    "        K = self.key(x)\n",
    "        V = self.value(x).permute(0, 2, 1)\n",
    "        attn_weights = torch.nn.functional.softmax(torch.bmm(Q, K) / (x.size(1) ** 0.5), dim=-1)\n",
    "        return torch.bmm(attn_weights, V).permute(0, 2, 1)\n",
    "\n",
    "class HRPredictionModel(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.cnn = CNNBlock()\n",
    "        self.attn = SelfAttention(embed_dim=128)\n",
    "        self.pool = torch.nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = torch.nn.Linear(128, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.cnn(x)\n",
    "        x = self.attn(x)\n",
    "        x = self.pool(x).squeeze(-1)\n",
    "        return self.fc(x).squeeze(1)\n",
    "\n",
    "def extract_signals_from_video(filepath, max_frames=600):\n",
    "    cap = cv2.VideoCapture(filepath)\n",
    "    green_signal, x_motion = [], []\n",
    "    prev_frame, count = None, 0\n",
    "\n",
    "    while cap.isOpened() and count < max_frames:\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "        roi = frame[100:200, 150:250]\n",
    "        green_signal.append(roi[:, :, 1].mean())\n",
    "        if prev_frame is not None:\n",
    "            x_motion.append(np.abs(roi.astype(np.float32) - prev_frame.astype(np.float32)).mean())\n",
    "        else:\n",
    "            x_motion.append(0.0)\n",
    "        prev_frame = roi\n",
    "        count += 1\n",
    "    cap.release()\n",
    "\n",
    "    green_signal = np.pad(green_signal, (0, max_frames - len(green_signal)), mode='edge')[:max_frames]\n",
    "    x_motion = np.pad(x_motion, (0, max_frames - len(x_motion)), mode='edge')[:max_frames]\n",
    "\n",
    "    green_signal = (green_signal - np.mean(green_signal)) / (np.std(green_signal) + 1e-8)\n",
    "    x_motion = (x_motion - np.mean(x_motion)) / (np.std(x_motion) + 1e-8)\n",
    "\n",
    "    sample = np.stack([green_signal, x_motion], axis=0)\n",
    "    return torch.tensor(sample, dtype=torch.float32).unsqueeze(0)\n",
    "\n",
    "# GUI with Enhancements\n",
    "def run_gui():\n",
    "    def load_video():\n",
    "        file_path = filedialog.askopenfilename(filetypes=[(\"Video files\", \"*.mp4 *.avi\")])\n",
    "        if file_path:\n",
    "            try:\n",
    "                progress.start()\n",
    "                result_var.set(\"Processing video...\")\n",
    "                root.update()\n",
    "\n",
    "                input_tensor = extract_signals_from_video(file_path).to(device)\n",
    "                with torch.no_grad():\n",
    "                    prediction = model(input_tensor).item()\n",
    "\n",
    "                result_var.set(f\"Patient: {name_entry.get()}\\nPredicted Heart Rate: {prediction:.2f} bpm\")\n",
    "                status_label.config(text=\"Prediction Complete\", fg=\"green\")\n",
    "\n",
    "            except Exception as e:\n",
    "                messagebox.showerror(\"Error\", f\"Failed to predict heart rate:\\n{e}\")\n",
    "                result_var.set(\"\")\n",
    "                status_label.config(text=\"Error occurred\", fg=\"red\")\n",
    "            finally:\n",
    "                progress.stop()\n",
    "\n",
    "    root = tk.Tk()\n",
    "    root.title(\"Heart Rate Estimator\")\n",
    "    root.geometry(\"460x300\")\n",
    "    root.configure(bg=\"#d2e8e4\")\n",
    "    \n",
    "    title = tk.Label(root, text=\"Heart Rate Predictor \", font=(\"Stencil\", 16, \"bold\"), bg=\"#c4efea\", fg=\"#40a499\")\n",
    "    title.pack(pady=15)\n",
    "    \n",
    "    frame = tk.Frame(root, bg=\"#d2e8e4\")\n",
    "    frame.pack(pady=10)\n",
    "    \n",
    "    # Add patient name input\n",
    "    name_label = tk.Label(root, text=\"Enter Patient Name:\", font=(\"Arial\", 12), bg=\"#d2e8e4\")\n",
    "    name_label.pack(pady=5)\n",
    "    \n",
    "    name_entry = tk.Entry(root, font=(\"Arial\", 12), width=30)\n",
    "    name_entry.pack(pady=5)\n",
    "    \n",
    "    browse_btn = tk.Button(frame, text=\"Browse Video\", command=load_video, font=(\"Big John\", 12), bg=\"#0995a7\", fg=\"white\", padx=10, pady=5)\n",
    "    browse_btn.pack(pady=10)\n",
    "    \n",
    "    result_var = tk.StringVar()\n",
    "    result_label = tk.Label(root, textvariable=result_var, font=(\"Script MJ Bold\", 18, \"bold\"), bg=\"#d2e8e4\", fg=\"#258573\")\n",
    "    result_label.pack(pady=20)\n",
    "    \n",
    "    status_label = tk.Label(root, text=\"\", font=(\"Broadway\", 16), bg=\"#d2e8e4\")\n",
    "    status_label.pack()\n",
    "    \n",
    "    progress = ttk.Progressbar(root, orient=\"horizontal\", length=250, mode=\"indeterminate\")\n",
    "    progress.pack(pady=10)\n",
    "    \n",
    "    footer = tk.Label(root, text=\"Developed using CNN + Self-Attention\", font=(\"Broadway\", 14), bg=\"#d2e8e4\", fg=\"black\")\n",
    "    footer.pack(side=\"bottom\", pady=5)\n",
    "    \n",
    "    root.mainloop()\n",
    "\n",
    "# Load model\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = HRPredictionModel().to(device)\n",
    "model.load_state_dict(torch.load(\"hr_model_self_attention_best.pth\", map_location=device))\n",
    "model.eval()\n",
    "\n",
    "# Launch GUI\n",
    "if __name__ == '__main__':\n",
    "    run_gui()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da4dea15-8fb0-4efc-bf59-c88684983534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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I3XLunsdr164pDz/8sBIeHq74+fkpXbp0UTZt2uRwzGVWKIlEUpzoFMUmpYlEIpFIJJISJycnhxYtWlC1alWrlUUikUjKG9IVSiKRSCSSEubhhx+md+/eREZGEhcXx9dff82RI0f49NNPS7tpEolEUmiksJBIJBKJpIRJSUlh4sSJXLlyBU9PT1q1asXKlSvp1atXaTdNIpFICo10hZJIJBKJRCKRSCQ3jEw3K5FIJBKJRCKRSG4YKSwkEolEIpFIJBLJDSOFhUQikUgkEolEIrlhbqrgbbPZzMWLFwkMDESn05V2cyQSiUQikUgkkjKNoiikpKRQpUoV9Pq8bRI3lbC4ePEiUVFRpd0MiUQikUgkEomkXBEbG0u1atXyXOamEhaBgYGAODBBQUGl0oacnBxWr15Nnz598PT0LJU2SAqGPGflD3nOyh/ynJVP5Hkrf8hzVv4o7XOWnJxMVFSUtR+dFzeVsFDdn4KCgkpVWPj5+REUFCRv6HKCPGflD3nOyh/ynJVP5Hkrf8hzVv4oK+fMnTACGbwtkUgkEolEIpFIbhgpLCQSiUQikUgkEskNI4WFRCKRSCQSiUQiuWFuqhgLiUQikUgkkqLEZDKRk5NT2s1wm5ycHDw8PMjMzMRkMpV2cyRuUNznzNPTE4PBUCTrksJCIpFIJBKJpIAoikJcXBzXr18v7aYUCEVRiIiIIDY2Vtb0KieUxDmrUKECERERN7x+KSwkEolEIpFICogqKsLDw/Hz8ys3nXSz2UxqaioBAQH5FjuTlA2K85wpikJ6ejrx8fEAREZG3tD6pLCQSCQSiUQiKQAmk8kqKkJDQ0u7OQXCbDaTnZ2Nj4+PFBblhOI+Z76+vgDEx8cTHh5+Q25R8oqSSCQSiUQiKQBqTIWfn18pt0QiKRrUa/lG44WksJBIJBKJRCIpBOXF/UkiyY+iupalsJBIJBKJRCKRSCQ3jBQWEokkb1avhhEjICmptFsikUgkkjJITEwMOp2OvXv35rncsWPHiIiIICUlpWQaVgpER0fzySef3NA6Jk+eTIsWLaz/P/fcczz//PM31rASQgoLiUSSN337wvz5MH58abdEIpFIJDfAqFGjMBgMhISE4OXlRfXq1XniiSe4du1aiWz/5ZdfZsyYMQQGBgKwfv16dDodOp0OvV5PcHAwLVu2ZNKkSVy6dKlA63ZX3JRHnnvuORYsWMCZM2dKuyn5IoWFRCJxj/XrS7sFEolEIrlB+vbty9GjRzl9+jTffPMNy5Yt48knnyz27Z4/f56lS5fy4IMPOsw7duwYFy9eZOfOnTz//POsXbuWJk2acODAgSJvR3Z2dpGvs7gJDw+nZ8+ezJgxo7Sbki9SWEgkEtekp2vfL14ERSm9tkgkEonkhvH29qZy5cpUq1aNPn36cM8997B69Wq7ZebMmUPDhg3x8fGhQYMGfPnll3bzd+zYQcuWLfHx8aFNmzbs2bMn3+3+9NNPNG/enGrVqjnMCw8PJyIignr16nHvvffyzz//EBYWxhNPPOF2u2rWrAlAy5Yt0el09OjRAxBWmiFDhjBt2jSqVKlCvXr1ALhw4QL33HMPISEhhIaGcvvttxMTE2Ndn/q7Dz74gMjISEJDQxkzZoxd1qT4+HgGDx6Mr68vNWvWZP78+Q77lpSUxKOPPkp4eDhBQUHccsst7Nu3z26Zd955h8qVKxMYGMjDDz9MZmamw3r69+/Pjz/+mM9RLn1kHQuJROIam4csOTlw6hTUqVNqzZFIJJIyi6LYD8aUFH5+UMiMPqdPn+bPP//E09PTOm3WrFm8/vrrfPHFF7Rs2ZI9e/YwevRo/P39eeCBB0hLS2PQoEHccsst/PDDD5w5c4bxbrjKbty4kTZt2rjVLl9fXx5//HEmTJhgra2QX7t27NhBu3btWLt2LY0bN8bLy8u6vnXr1hEUFMSaNWusBeF69uxJ165d2bhxIx4eHrz11lv069eP/fv3W3/7999/ExkZyd9//83Jkye55557aNGiBaNHjwaE+IiNjeWvv/7Cy8uLcePGWQvNgSg+N3DgQCpWrMjKlSsJDg5mxowZ3HrrrRw/fpyKFSvy008/8frrrzN9+nS6du3K999/z2effUatWrXsjkmrVq2IjY3l7Nmz1KhRw63jWBpIYSGRSFyT25/zn3+ksJBIJBJnpKdDQEDJbzc1Ffz93V58xYoVVKtWDZPJZB0Z/+ijj6zz33zzTT788EOGDRsGCEvA4cOHmTFjBg888ADz58/HZDIxe/Zs/Pz8aNy4MefPn3ewLuQmJiaG1q1bu93OBg0aWH8XHh6eb7vCwsIACA0NJSIiwm5d/v7+fPPNN1bBMHv2bPR6Pd988401zeqcOXOoUKEC69evp0+fPgCEhITwxRdfYDAYaNCgAQMHDmTdunWMHj2a48eP88cff7Bt2zbat28PwLfffkvDhg2t2/377785cOAA8fHxeHt7A/DBBx+wZMkSfvnlFx599FE++eQTHnroIR555BEA3nrrLdauXetgtVArYsfExEhhIZFIyimnT9v/f/ly6bRDIpFIJEVCjx49eO+999Dr9cyePZvjx4/z1FNPAXDlyhViY2N5+OGHraPyAEajkeDgYACOHDlC8+bN7YoDduzYMd/tZmRk4OPj43Y7FYvrrU6nc6tdedG0aVM7C8bu3bs5efKkNYhcJTMzk1OnTln/b9y4sV0V6sjISGvcx5EjR/Dw8LCzwjRo0IAKFSrYbSc1NdWhOntGRoZ1O0eOHOHxxx+3m9+xY0f+/vtvu2lqdez00rCKFQApLCQSiWtyWyyMxtJph0QikZR1/PyE9aA0tlsA/P39qVWrFkFBQXz22Wf07NmTKVOm8Oabb2I2mwHhDqWOwquoHWylkLF2lSpVKlD2qSNHjgAifas77coL/1wWHbPZTOvWrZ3GRKiWD8DORQyEyFHbYit8XGE2m4mMjGS9k+QntgLEHdRjZ9u+sogUFhKJxDW5hYVN0JpEIpFIbNDpCuSSVFZ4/fXX6d+/P0888QRVqlShatWqnD59muHDhztdvlGjRnz//fdkZGRYR9G3bduW73ZatmzJ4cOH3WpTRkYGM2fOpFu3btaOdH7tUi0SJpMp3/W3atWKRYsWWQOqC0PDhg0xGo3s2rWLdu3aASK71fXr1+22ExcXh4eHB9HR0S7Xs23bNkaOHGmd5ux4HjlyBE9PTxo3blyo9pYUMiuURCJxjRq8bcm2IYWFRCKR/Lfo0aMHjRs3ZurUqYAozjZt2jQ+/fRTjh8/zoEDB5gzZ441DuP+++9Hr9fz8MMPc/jwYVauXMkHH3yQ73b69u3L1q1bnXb84+PjiYuL48SJE/z444907tyZq1ev8tVXX1mXya9d4eHh+Pr68ueff3L58mWS8ijqOnz4cCpVqsTtt9/Opk2bOHPmDBs2bGD8+PGcP3/ereNWv359+vXrx+jRo9m+fTu7d+/mkUcesYotgF69etGxY0eGDBnCqlWriImJYcuWLbzyyivs2rULgPHjxzN79myrW9rrr7/OoUOHHLa3detWunbtarf+sogUFhKJxDXqg7lyZfEpXaEkEonkP8czzzzDrFmziI2N5ZFHHuGbb75h7ty5NG3alO7duzN37lxrOteAgACWLVvG4cOHadmyJS+//DLvvvtuvtsYMGAAnp6erF271mFe/fr1qVKlCq1bt+add96hV69eHDx4kEaNGlmXya9dHh4efPbZZ8yYMYMqVapw++23u2yLn58fGzdupHr16gwbNoyGDRvy0EMPkZGRUSALxpw5c4iKiqJ79+4MGzbMmlZWRafTsXLlSrp168ZDDz1kTacbExNDZct79Z577uG1117j+eefp3Xr1pw9e9ZpIPyvv/7Kww8/7HbbSgudUlhnuXJIcnIywcHBJCUlFdr0daPk5OSwcuVK6w0mKfvc1OescmWIj4f+/eGPP+CZZ+DDD0u7VflyU5+zcoo8Z+WTm/W8ZWZmcubMGWrWrFmggOSygNlsJjk5maCgIPT6kh1f/vLLL/n9999ZtWpViW63vLNs2TKee+45u1S4RU1e13RB+s8yxkIikbhGDURUg8ykK5REIpFICsmjjz7KtWvXSElJccjIJHFNWloaX3zxBR4eZb/bXq5coS5cuMCIESMIDQ3Fz8+PFi1asHv37tJulkTy38Rs1oo9qcJCukJJJBKJpJB4eHjw8ssvS1FRQO6++263iwuWNmVf+li4du0anTt3pmfPnvzxxx+Eh4dz6tSpAqfrkkgkbmKbK1taLCQSiUQikeRDuREW7777LlFRUcyZM8c6zVXqLolEUgSkpYlPnQ5Un0opLCQSiUQikbig3LhCLV26lDZt2nDXXXcRHh5Oy5YtmTVrVmk3SyL576LGV/j7gxosJl2hJBKJRCKRuKDcWCxOnz7NV199xTPPPMNLL73Ejh07GDduHN7e3nZFRWzJysoiKyvL+n9ycjIgsljklNLIq7rd0tq+pODctOfs2jU8AcXfH7NejwEwZ2VhKgfH4aY9Z+UYec7KJzfrecvJyUFRFMxms7USc3lBTQaqtl9S9imJc2Y2m1EUhZycHIdq5gW5v8tNulkvLy/atGnDli1brNPGjRvHzp072bp1q9PfTJ48mSlTpjhMX7BgAX5+fsXWVonkv0DI0aN0e+EFUiMiOHX77TSfMYOLHTqw84UXSrtpEolEUqp4eHgQERFBVFRUsaX/lEhKkuzsbGJjY4mLi8OYyzshPT2d+++//7+VbjYyMtKuUAqIMui//vqry9+8+OKLPPPMM9b/k5OTiYqKok+fPqVax2LNmjX07t37psr5XZ65Wc+ZzrKv/mFhNG7ZEoCI0FAGDBhQms1yi5v1nJVn5Dkrn9ys5y0zM5PY2FgCAgLKXR0LRVGs6V51Ol1pN0fiBiVxzjIzM/H19aVbt25O61i4S7kRFp07d+bYsWN2044fP06NGjVc/sbb2xtvb2+H6Z6enqX+ACwLbZAUjJvunFncCHWBgXhYHjJ6kwl9OToGN905+w8gz1n55GY7byaTCZ1Oh16vL/EiczeK6kqjtl9S9imJc6bX69HpdE7v5YLc2+XmipowYQLbtm1j6tSpnDx5kgULFjBz5kzGjBlT2k2TSP6b2AZvqw+Vm8yPWiKRSCRlh//9739MnTq1tJtRbMydO7dIyijodDqWLFkCQHx8PGFhYVy4cOGG1+sO5UZYtG3blsWLF7Nw4UKaNGnCm2++ySeffMLw4cNLu2kSyX8TVVgEBIBa7VMKC4lEIinXxMXF8cILL1CvXj18fHyoXLkyXbp04euvvybdtn5RGWP//v2sWLGCp556yjqtR48e6HQ6dDod3t7eVK1alcGDB/Pbb78VeP2jRo1iyJAhRdjiskF4eDj/+9//eP3110tke+XGFQpg0KBBDBo0qLSbIZHcHKh1LAICNIuFTDcrkUgk5ZbTp0/TuXNngoKCeOutt2jevDlGo5Hjx48ze/ZsqlSpwm233eb0tzk5OaXq7vbFF19w1113OVTtHj16NG+88QY5OTlcuHCBxYsXc++99zJq1ChmzpxZ5O0o7eNQGB588EHatWvH+++/T0hISLFuq9xYLCQSSQkjXaEkEonkP8WTTz6Jh4cHf/31F3fffTcNGzakadOm3HHHHaxYsYLBgwdbl9XpdHz99dfcfvvt+Pv789ZbbwGwbNkyWrdujY+PD7Vq1WLKlCl2WYSSkpJ49NFHCQ8PJygoiFtuuYV9+/ZZ50+ePJkWLVrw/fffEx0dTXBwMPfeey8pKSku2202m/n555+dih4/Pz9rhq4OHTrw7rvvMmPGDGbNmsXatWuty124cIF77rmHkJAQQkNDuf3224mJibG2ad68efz+++9WC8j69euJiYlBp9Px008/0aNHD3x8fPjhhx8AmDNnDg0bNsTHx4cGDRrw5ZdfWrel/u63336jZ8+e+Pn50bx5c4cspnPnzqV69er4+fkxdOhQEhISHPZv2bJltG3bloiICOrUqeNwvE+cOGENuG7UqBFr1qxxWEfTpk2JiIhg8eLFLo9xUSGFhUQicY6txUK6QkkkEkmeKIpCWnZaif+5WzUgISGB1atX8+STT+Lv7+90mdwZh15//XVuv/12Dhw4wEMPPcSqVasYMWIE48aN4/Dhw8yYMYO5c+fy9ttvW4/BwIEDiYuLY+XKlezevZtWrVpx6623kpiYaF3vqVOnWLJkCcuXL2f58uVs2LCBd955x2Xb9+/fz/Xr12nTpo1b+/rAAw8QEhJidYlKT0+nZ8+eBAQEsHHjRjZv3kxAQAD9+vUjOzubiRMncvfdd9OvXz8uXbrEpUuX6NSpk3V9zz//POPGjePIkSP07duXWbNm8fLLL/P2229z5MgRpk6dyquvvsq8efPs2vHyyy8zceJE9u7dS7169bjvvvusomD79u089NBDPPnkk+zdu5eePXtaxZuKerzHjh3Ltm3b+Oqrr+yOt9lsZtiwYRgMBrZt28bXX3/N888/7/SYtGvXjk2bNrl1/G6EcuUKJZFIShBnFgvpCiWRSCROSc9JJ2BaQIlvN/XFVPy9nAsFW06ePImiKNSrV89ueqVKlcjMzARgzJgxvPvuu9Z5999/Pw899JD1///973+88MILPPDAAwDUqlWLN998k0mTJvH666/z999/c+DAAeLj461ZOT/44AOWLFnCL7/8wqOPPgqIDvHcuXOtbk3/+9//WLdunbXDnJuYmBgMBgPh4eFuHRO9Xk+9evWsFokff/wRvV7PN998YxVPc+bMoUKFCqxfv54+ffrg6+tLVlYWERERDut7+umnGTZsmPX/N998kw8//NA6rWbNmlahpR4bgIkTJzJw4EAApkyZQuPGjTl58iQNGjTg008/pW/fvrxgqQ1Vr149tmzZwp9//mn9/dtvv2093snJyTRr1szueK9du5YjR44QExNDtWrVAJg6dSr9+/d32IeqVauyZ88et47fjSCFhUQicY6zGAtpsZBIJJJyTW6rxI4dOzCbzQwfPpwsS5pxldwWgt27d7Nz5047AWAymcjMzCQ9PZ3du3eTmppKaGio3e8yMjI4deqU9f/o6Gi7WInIyEji4+NdtjkjIwNvb+8C1XBQFMW6/O7duzl58qRDfEZmZqZdu1xhexyuXLlCbGwsDz/8MKNHj7ZONxqNBAcH2/2uWbNm1u+RkZGAyNLUoEEDjhw5wtChQ+2W79ixo52wyO94HzlyhOrVq1tFhboOZ/j6+pZIcL4UFhKJxDkyK5REIpG4jZ+nH6kvppbKdt2hTp066HQ6jh07xi233GKdXqtWLUB0PHOT22XKbDYzZcoUu9F7FR8fH8xmM5GRkaxfv95hvm0a1dzBzzqdzlqrwRmVKlUiPT2d7Oxstyqdm0wmTpw4Qdu2ba3tbt26NfPnz3dYNiwsLN/12R4HtZ2zZs2iffv2dssZDAa7/233UxU56u/dcWFTj/eQIUNITU0lICDAWsfCx8fH6Tpcia/ExES39vVGkcJCIpE4R7pCSSQSidvodDq3XJJKi9DQUHr37s306dMZOXIkQUFBBV5Hq1atOHbsGHXq1HE5Py4uDg8PD6Kjo2+wxRotWrQA4PDhw9bveTFv3jyuXbvGHXfcYW3XokWLrAHlzvDy8sJkMuW77sqVK1O1alVOnz59QyUPGjVqxLZt2+ym5f7f9ngnJycTFBRkVyCvUaNGnDt3josXL1KlShUAhwBxlYMHD9KjR49Ct9ddZPC2RCJxjnSFkkgkkv8UX375JUajkVtuuYVFixZx5MgRjh07xg8//MDRo0cdRtxz89prr/Hdd98xefJkDh06xJEjR1i0aBGvvPIKAL169aJjx44MGTKEVatWERMTw5YtW3jllVfYtWtXodsdFhZGq1at2Lx5s8O89PR04uLiOH/+PNu3b+f555/n8ccf54knnqBnz54ADB8+nEqVKnH77bezadMmzpw5w4YNGxg/fjznz58HhHvW/v37OXbsGFevXiUnj/fd5MmTmTZtGp9++inHjx/nwIEDzJkzh48++sjtfRo3bhx//vkn7733HsePH+eLL76wc4MC7XhPmTKFI0eOOD3e9evXZ+TIkezbt49Nmzbx8ssvOz1Gu3fvpk+fPm63r7BIYSGRSJxja7GQrlASiURS7qlduza7d++me/fuvPzyyzRv3pw2bdrw+eefM3HiRN588808f9+3b1+WL1/OmjVraNu2LR06dOCjjz6iRo0agLDarFy5km7duvHQQw9Rr1497r33XmJiYqhcufINtf3RRx916so0a9YsIiMjqV27NkOHDuXw4cMsWrTILv2rn58fGzdupHr16gwbNoyGDRvy0EMPkZGRYbVgjB49mvr169OmTRvCwsL4559/XLblkUce4ZtvvmHu3Lk0bdqU7t27M3fuXGrWrOn2/nTo0IFvvvmGzz//nBYtWrB69WqrYFBRj/fatWu59dZb6dSpk93x1uv1LF68mKysLNq1a8cjjzziNAD+999/p3r16nTt2tXt9hUWneJunrL/AMnJyQQHB5OUlFQoE2BRkJOTw8qVKxkwYEC5K7Bys3LTnrM6deDUKfjnHwgNhQYNoEIFuHattFuWLzftOSvHyHNWPrlZz1tmZiZnzpyhZs2a+Pj4lHZzCoTZbHbqVlPWyczMpH79+vz4448uA5T/q9zoOWvXrh1PP/00999/v8tl8rqmC9J/Lj9XlEQiKVlkgTyJRCKRlBF8fHz47rvvuHr1amk3pVwRHx/PnXfeyX333Vci25PB2xKJxDmyQJ5EIpFIyhDdu3cv7SaUO8LDw5k0aVKJbU8KC4lE4ojZbC8s1DSAMiuURCKRSCQSF0hXKIlE4khGBqjhV7auUGazJjIkEolEIpFIbJDCQiKROKJaKwD8/DRhAdIdSiL5r5OZCd26waOPlnZLJBJJOUMKC4lE4ogauO3nB3q9FmMB0h1KIvmv88MPsGkTzJpV2i2RSCTlDCksJBKJI7bxFSAtFhLJzcT69aXdAolEUk6RwkIikTiiWixUYWFrsZDCQiIpXr79Fp57TotzKmm2bNG+y5gqiURSAGRWKIlE4ohqsfD3F596vfgzm6WwkEiKE0WBRx4R34cOhU6dSnb7SUlw5oz2f3Y2lLMCcBKJpPSQFguJROJIbosFaO5QMsZCIik+kpO17+p9WJKsW2f/f3Z2ybdBUqrExMSg0+nYu3dvsW8rOzubOnXq8M8//xT7tkqLUaNGMWTIkBtax/r16wkJCeH69esALF++nJYtW2IugxZFKSwkEokjtlW3VWT1bYmk+Ll0SfteGvfa0qX2/0th8Z9i1KhRGAwGQkJCMBgMhIaG0q9fP/bv318q7Zk5cyY1atSgc+fO1mk6nc765+/vT926dRk1ahS7d+8u8Pqjo6P55JNPirDFZYNBgwah0+lYsGBBaTfFASksJBKJI7mDt0FW35ZISoKLF7Xv166V7LaNRli+3H5aVlbJtkFS7PTt25ejR49y4cIF1q1bh4eHB4MGDSqVtnz++ec8orr+2TBnzhwuXbrEoUOHmD59OqmpqbRv357vvvuuyNtgMpnK5Mh/fjz44IN8/vnnpd0MB6SwkEgkjuRlsZCuUBJJ8WFrsbC4PRQ7apD49u2QkAAhIeDlJaZJi8V/Dm9vbypXrkxERAQtWrTg+eefJzY2litXrjhdfu7cuVSoUMFu2pIlS9DpdHbTli1bRuvWrfHx8aFWrVpMmTIFYx7vi3///ZeTJ08ycOBAh3kVKlQgIiKC6Oho+vTpwy+//MLw4cMZO3Ys12wE95YtW+jWrRu+vr5ERUUxbtw40iwDYz169ODs2bNMmDDBagGx3Z/ly5fTqFEjvL29OXv2LNnZ2UyaNImqVavi7+9P+/btWW+TIU393apVq2jYsCEBAQH069ePSzb3rMlk4plnnqFChQqEhoYyadIklFxJGBRF4b333qNWrVr4+vrSvHlzfvnlF7tlVq5cSb169fD19aVnz57ExMQ4HKPbbruNHTt2cPr0aZfHuDSQwkIikTjizGIhXaEkkuLH1mJREsLip58gIgI2boQDB8S0Tp20QQVpsXAbRRGPzpL+u5HkYampqcyfP586deoQGhpa6PWsWrWKESNGMG7cOA4fPsyMGTOYO3cub7/9tsvfbNy4kXr16hEUFOTWNiZMmEBKSgpr1qwB4MCBA/Tt25dhw4axf/9+Fi1axObNmxk7diwAv/32G9WqVeONN97g0qVLdgIgPT2dadOm8c0333Do0CHCw8N58MEH+eeff/jxxx/Zv38/d911F/369ePEiRN2v/vggw/4/vvv2bhxI+fOnWPixInW+R9++CGzZ8/m22+/ZfPmzSQmJrJ48WK7/XjllVeYM2cOX331FYcOHWLChAmMGDGCDRs2ABAbG8uwYcMYMGAAe/fu5ZFHHuGll15yOB41atQgPDycTZs2uXX8SgqZFUoikTjiLHhbukJJJMVPSQuLe+4Rn927w6RJ4nutWrBrl/guLRZuk55u/8gsKVJT7Y3L+bFixQqqVasGQFpaGpGRkSxfvhy9vvBjzW+//TYvvPACDzzwAAC1atXizTffZNKkSbz++utOfxMTE0OVKlXc3kaDBg2svwN4//33uf/++3n66acBqFu3Lp999hndu3fnq6++omLFihgMBgIDA4mIiLBbV05ODl9++SXNmzcH4NSpUyxcuJDz589b2zRx4kT+/PNP5syZw9SpU62/+/rrr6lduzYAY8eO5Y033rCu95NPPuHFF1/kjjvuAODrr79m1apV1vlpaWl89NFH/PXXX3Ts2NF6rDZv3syMGTOsba9VqxYff/wxOp2O+vXrs3//ft577z2HY1K1alWn1ozSRAoLiUTiSO50syBdoSSSksDWFaqkYyzUDkp0NHh7i+9SWPzn6NGjB++99x4BAQFcv36dL7/8kv79+7Njxw5q1KhRqHXu3r2bnTt32lkoTCYTmZmZpKen4+fn5/CbjIwMfAqQylh1KVJdmnbv3s3JkyeZP3++3TJms5kzZ87QsGFDl+vy8vKiWbNm1v///fdfFEWhXr16dstlZWXZWXL8/PysogIgMjKS+Ph4AJKSkrh06ZJVMAB4eHjQpk0ba9sPHz5MZmYmvXv3tttOdnY2LVu2BODIkSN06NDBztWsQ4cOTvfD19eX9PR0l/tZGpRbYTFt2jReeuklxo8f/5+M+JdISpW80s1Ki4VEUnyUtMWiShVtm2r9ipo1tRgL6QrlNn5+pZMh2EmfPU/8/f2pVasWQUFB6PV6WrduTXBwMLNmzeKtt95yWF6v1zvECeTkeg+YzWamTJnCsGHDHH7vSjxUqlSJA6r7nRscOXIEgJo1a1q3+dhjjzFu3DiHZatXr57nunx9fe067mazGYPBwO7duzEYDHbLBti8Bz3V96AFnU7ncGzyQg0SX7FiBVWrVrWb520R8wVZX2JiImFhYW4vXxKUS2Gxc+dOZs6caac2JRJJEeIseFu6QkkkxU9JB2/XqqUJi1OnxKetsJAWC7fR6QrmklRW0Ol06PV6MjIynM4PCwsjJSWFtLQ0/C07mLvGRatWrTh27Bh16tRxe7stW7bkq6++QlEUh0BwZ3zyyScEBQXRq1cv6zYPHTqU5za9vLwwmUxutcVkMhEfH0/Xrl3d3gdbgoODiYyMZNu2bXTr1g0Ao9HI7t27adWqFYA1WPzcuXN0797d6XoaNWrEkiVL7KZt377dYbnMzExOnTpltXSUFcpd8HZqairDhw9n1qxZhISElHZzJJL/JnkFb0tXKImkeFCUkk83a+vjnpgoPqUr1H+arKwsLl++TFxcHEeOHOGpp54iNTWVwYMHO12+ffv2+Pn58dJLL3Hy5EkWLFjA3Llz7ZZ57bXX+O6775g8eTKHDh3iyJEjLFq0iFdeecVlO3r27ElaWhqHDh1ymHf9+nXi4uI4e/Ysa9as4c4772TBggV89dVX1gxVzz//PFu3bmXMmDHs3buXEydOsHTpUp566inreqKjo9m4cSMXLlzg6tWrLttSr149hg8fzsiRI/ntt984c+YMO3fu5N1332XlypV5HE17xo8fzzvvvMPixYs5evQoTz75pLWoHUBgYCATJ05kwoQJzJs3j1OnTrFnzx6mT5/OvHnzAHj88cc5deoUzzzzDMeOHWPBggXWebZs27YNb29vO9erskC5ExZjxoxh4MCBVsUqkUiKAekKJZGUPAcPighglZJKN2tLhQriz5Ur1NWrMGuWfYVwSbli1apVNGjQgKpVq9K+fXt27tzJzz//TI8ePZwuX7FiRX744QdWrlxJ06ZNWbhwIZMnT7Zbpm/fvixfvpw1a9bQtm1bOnTowEcffZRnzEZoaCjDhg2zi5FQefDBB4mMjKRBgwY88cQTBAQEsGPHDu6//37rMs2aNWPDhg2cOHGCrl270rJlS1599VUiIyOty7zxxhvExMRQu3btfF2G5syZw8iRI3n22WepX78+t912G9u3bycqKirP39ny7LPPMnLkSEaNGkXHjh0JDAxk6NChdsu8+eabvPbaa0ybNo2GDRvSt29fli1bZnXxql69Or/++ivLli2jefPmfP31105d1BYuXMjw4cOdxq+UJjqlIM5cpcyPP/7I22+/zc6dO/Hx8aFHjx60aNHCZYxFVlYWWTYPxeTkZKKiorh69arb6c2KmpycHNasWUPv3r0dfPUkZZOb8Zx5tG2Lbt8+jMuXo/TpA4ChRw/0W7ZgXLQIJdeDsqxxM56z8o48Z6CfPBnD1KkodeuiO3ECJSQE4+XLxbpNwz33oLdJh6k0b45x504Mt9yCfvNmjAsXolgy3AAYOndGv3MnptGjMU+fftOet8zMTGJjY4mOji5QAHJZQFEUUlJSCAwMdMsFqTg5cOAAffr04fjx4wQGBpZqW8oyuc/ZlStXaNSoETt27LAKkhslMzOTmJgYoqKiHK7p5ORkKlWqRFJSUr7953ITYxEbG8v48eNZvXq12zfxtGnTmDJlisP01atXl7rCU/MwS8oPN9M5uzU+ngBg64EDJFpcnzonJVEJ2LNzJxdVN4kyTlk/Zx5paRj9/IRzdjHge/kymaGhKB7l5lFf5s9ZcWHIzKTH3LkEAIc6d6bJiROQlMTK5cvhBtKA5kfbS5ewTfh50d+fXStX0jElhXBg344dnPf1tc6/fedOAHJ++YVVNoXNbrbz5uHhQUREBKmpqWSXU3exlJSU0m4CNWrUYPLkyRw8eJDGjRuXdnPKPOo5O3jwIO+//z6hoaEkF5H1MDs7m4yMDDZu3OhQ2LAgmafKjcViyZIlDB061C5a32QyWYOOsrKyHCL5pcVCUhTcjOfMo0YNdJcukbNjB7RoAYChf3/069ZhnDsXxcYcXRYpD+dMt2EDHr17Y3ruOcx5FJEq9PrXr8ejTx/Md9yBaeHCIl9/UVMezlmxce0aHm3aoIuNRfHxwXjyJJ6WOgM5V65AcHCxbdowbBj65csBMD/4IKZnnoH69THcfjv6P/7AOGsWiqU2AYCnxUXK3KEDpo0bb9rzJi0WkpKkJM7ZTWexuPXWWx3Skj344IM0aNCA559/3kFUgEjd5e1kZNXT07PUH4BloQ2SgnFTnTNLjIVnSIgWW2HpUHgoijatjFOmz9nIkQAY3n8fg5PCRzeMRazof/0VfVk9Bk4o0+esuDhzBmJjwc8P3c8/41m1qgiezsrCMy0NKlUq/jZ8+y36hx7SAi8tHQsPk0m739WkDoC+Vi2768rhvGVkwPHjYClA9l/DdmDzRgrLlQZqylO1/ZKyT0mcM71ej06nc/oMLsgzudxcUYGBgTRp0sTuz9/fn9DQUJo0aVLazZP8V9i+HW67DY4dK+2WlB6K4rxAnupOI7NCFQ22aUWLg/Pni3f9kqJDTYcZGQkDBojvlsw3xR7ArW47d2fFWfD2iRPa97xKTKemQqdOwtq5YUORNFMikZQPyo2wkEhKhOnTYdkyWLSotFtSehiNYBkdwdYcKrNCFR22edWLy83lwoXiWa+k6FHvN1vLe0kLi9xWf2d1LGwHXPKKK3jqKVDrHBQ29sJohE8+ASepSCUSSdml3LhCOWP9+vWl3QTJf43Tp8VnAQKV/nPYWiRsg36lsCg6LBVkAahdu3i2kZlZPOuVFD3OOvdqgpHifhY5EzXgvI7F8ePa97wqctvm/d+zp3DtevppMdBTuzacPFm4dZQAqouKRFLeKapruVwLC4mkyDlzRnzezJ0yV8JCukIVHTt2aN+Lo2MiawyUL5y5I6nCwkU15GLdNjh3hbJkhAJcWyxSUiA+Xvt/927hXlnQgNPp08WnWg28jOHl5YVer+fixYuEhYXh5eVVbgKhzWYz2dnZZGZmyhiLckJxnjNFUcjOzubKlSvo9Xq81Hu/kEhhIZGoZGZqVW+lsBBIi0XxsHu39r04UlXajiyDEC+yA1F2cWaxUFO8lhWLxbZtwk1UxdV1q1p9fX3FMpcvi+dq1arut+nsWe17SQSuu0N6utgni3jQ6/XUrFmTS5cucdG2Wno5QFEUMjIy8PX1LTdi6GanJM6Zn58f1atXv2HhIoWFRKJi+zLLy8z/X8fW/9+2syGFRdGh+p9D8QiL3MkHMjLsA/ElZQtnnfuyZrFQK/96eopnQH7CokkT0faDB4WQLoiwsCnYR0iI+78rLrZuhW7d4PnnteOAsFpUr14do9GIyfa5WcbJyclh48aNdOvW7ebLwFZOKe5zZjAY8PDwKBLRIoWFRKKiukGBtFiA6GjYdjZU60VRC4vCuEmUZ8xm2LdP+784hEXujFBpaVJYlGXycoUqbouFO8HbiiI61yBiH95/3/V1q7ou1a4t1n3woP2z1R1sM0mVhXi3wYPFc/Htt+2EBeAyPWdZxmAwYDQa8fHxKVftvpkpT+dM2sYlEhUpLASqsMhdrVl9mBVljMXzz0NEBMTEFN06yzonT9rVAygWC1BCgv3/ttuTlD3ycoUqbouFO65QFy5AYqJ4JrRsKaa7suraCgtVnBRkNN9shk2btP+Le//dIff9JJFIXCKFhUSiIoWFwJWwUDsaRfWiT0uD994TgZ62WWT+66huUIGB4rM4LBa5O0JlYdRX4pq8XKFKymKRlyuUes02aABq1V13LBbqOguSoODIEfvrt7Sv3dhY7XvjxqXXDomknCCFhUSiIoWFwJWwiIwUn0UVqGgrJsLDi2ad5QG1k9aunfgsCWEhLRZlG2ed+9IO3rZ1hVKv2RYtnNe3sEVNDVurlrbOggiLjRvFZ9Om4jMzs3gyp7nL2rXa97yKAkokEkAKC4lEQwoLgSthUaOG+LQNcr8RfvpJ+16OAh9vGDWvvxQWEpW86liUVvC2aqHMytJiglq0cF7fQuX4cfEcNRhE8La6zoLc32oq5l69tGml+Ty2rcMhE1dIJPkihYVEoiKFhcCVsKheXXyeO1c02/nnH+37zSQsclss/gsxFv/+a1/0T1IwnFkNSspi4U7w9q5d4nvz5nlbLBYuFJ99+kBoaOFcoVQR06mTNq004ywOH9a+S2EhkeSLFBYSCYiCYomJ2v9SWLgWFpcu3fgoe3Y2xMU5bvO/Tlyc+NPpoHVrMc1sLnphpQoLNVVncQqLixfFvjRqVHzbKC9cuADDhtnXe3CH0rRY5Be8ffSoSK7g4QEdOjgvnAcic9SCBeL7/ffbr9NdYZGTA4cOie+tWmkJI0ozzkJtD0hhIZG4gRQWEgk4pkOUwsJRWISFgY+P6EDkTmdaUC5cEOtRuVksFqq1on59qFBBm16U7lCKogkLVQwWp7DYvFn7frMIRFd88YWowXDbbfDnn+7/rjRjLPIL3laDsTt2FDEGriwW169rhRlvu81+ne4Ki6NHxXqDgiA6uuTElSsSE+0HQEpLWNwsz0fJfwIpLCQS0ISFj4/4lMLCUVjodFpHtaBxFmlp9kLCNtMK3DwvTmdBsFC0wiIpSTueJSEsTpzQvpdEBzAjw7GyeFnhjz+07yNH2ltB86I0C+TlF7yt0ru3/fTc16x6T4eGapmjChpjobpBNWsmfltS4soVtm5QUDrC4qWXRPVxNSheIinjSGEhkYAmLOrXF59SWDgKC9ACuAsSZ7Fli3gxPvusNi23sLhZRroPHhSfzZtrbh5g32EZPx66dCl89XfVWuHnJzp5ULzCwrbKd3F3ghUFevQQ96ltUG1Z4PJlrWMcFgZXrsCkSe79Nq86FqVlsVBdoVRuvdV+ei5hobtwQXyJitImFtQVylZ4Q+lbLFRhEREhPktDWEybJqxBX39d8tuWSAqBFBYSCWjComFD8WkrLBSFRnPnop8+veTbVRqonfzcI5hQuADuXr3E8fz4Y23azWqxUN0qoqJER04Vb2onLTVVuNP8848mQgqKKixCQ7Vq28UpLA4c0L4XtyBftkzLGmQb/F8WWL1afLZqBb/9Jr5/+61wjcqPvCpvl1RWqPwsFs2a2U/PbbFQhUW1atq0grpCrVsnPtXEBqVtsVBFs5r6tqQHQGwHF26mlNySco0UFhIJOAqLrCyr645u40bqLlmCYcKEUmpcCZOXxaKgwiI+3nnH6GYVFleuiM+wMPGpWi3UTtr27VonLCmpcNsoSWGRk2PvLlKcnWBFgcmTtf9TUopvW4VhyxbxeeutwuKkWitsLXWuKM2sUPkFbwNUrKjVcLCtpm1z3+rUe9rWYlEQV6jz54XFQqeDfv3EtNK2WKhiqXZt8VnSFgtb96eKFUt22xJJIZHCQiIBzU9czWyjKNaXiG7/fm250izUVFLkJSyCg8WnOx3VtWuhZk3t/wYNtO+5g79vFlcoVVhUqiQ+c4/+qp1TEO4PhaEkhYUabKtSnB3AvXvt3Z8uXy6+bRUG1Y2nVSvx+eST4tOdgpJlsY6FrcVCHVDIPd3m3Otu1GKxYoX47NBBE96lbbFQz53qAlrSwsLWzbCwrpESSQkjhYVEkpWljQypnQLQ3DpOn9amlWbaw5IiL2GRX9VdWxYutD9etm4y6uim2sG+GSwWigJXr4rvasdJPZ5qh8VWWJQHi4VtRigoXleo776z/z8+vvi2VVBMJs0lrHlz8WlbYM42cYGr30PpZoXKyxXKDWFhHSywFRYFibFYtUp8DhyoTStti0VpC4ujR7XvxVFIUyIpBqSwkEhOnBAv1+BgkeJQxdJJ0qnpFuHmEBZqR+NGhYUayPr66+LTtnOgCgv1eN8MwiIpSeuY5BYW2dmi87V1q7Z8YS0Waoe7UqXiFxYbN9r/X5wdwEWLxOd994nPsmSxOHVKHGNfX6hXT0yzdSXKr0NaFrJC5RW8rXaswbXFQhUWhXWFUoVZx47atNK0WChK6QsLabGQlEOksJBIVB/xRo3Ei1B9oarCwtaHvCRecFevCl/7/EY5i4uisFgYjVrwcYcO4lPtICUmaiP3devab/O/jLrPAQFaWmPbGIvDh+2tFIW1WKjHvUEDR2GxYYN7wcTuoChifbYUVyc4MVEUZgS45x7xWZYsFqqIbtpUEwe2HfP8OoV5uUKlpxfvs6CgFgudzjE2SFFuLHg7M1OzDKtxblC6FoukJG27qrBQlJIdBLG1WEhhISknSGEhKThZWfDggzB/fmm3pGiwFRZgX8siKQmdbaBycWbXUbnvPtEZV322S5qiEBbHj4vrJCBAO66qm8yRI+KzenUtZuNmsFjkDtwG++Np6wYFhbdY2KbstBUWs2aJVK133FE0nfJTp0Rn38tLqyJeXB1A1WoYGanF7ZQli4V6zFU3KCicsHDmCqUoxesG404dC1thYTvPsl+eqano1GdjYVyhTpwQywQHa6ldwV5clTSqtaJCBQgM1KaXpNUiJkb7LoWFpJwghYWk4Hz+OcydCyNGlHZLioa8hMXKlfbLFvcLLitLcy9RRwBLmqIQFraFrtTOQXa26EAdOiT+b9RI28bNJCzUuBKwj7FQ06eqYqAwFovERC1jV7Nm2rqOHoUnnhDfbStz3wi7donPVq0gJER8L64YC1VY1K6tpd28erXsWLrU9tmOthsMWsf6RiwWULzPHXfqWNi6N4HDcyBMvd/r1NEEke0687u/1cGGhg2FRURFXVdpWCxUYVGliv2zsKSERVaW/QCAFBaScoIUFpKCU9byx98oroRFUpKoempLcVss9u/XOu2lFaxXlMKieXPteILoeNoeb7UjVVY6iMWJuxaLXr3EZ2EsFupxr1VLjP6qnfDERPvOXVF0VNXz2LRpwTuAJpPoNLnbSbMVFpUqic5nUQmkokDthFataj/dNoA7L5xZDTw9tf9LQljktljYFnCMjLSfl+s5UFV9J9x5p/1y7rhCJSXBn3+K77aZ48C1xWLqVGGRc7eyeWGwFRa2x6KknlWq65+KFBaScoIUFpKCo9Z8gPL/sMvJEW474Cgsfv0VYmJQIiNJUc37xW2xUIt/QdkWFvmdd9Vfun59+xFMW2HRuLHWmbmZLBa2wkLtsMTFaZnJ1Bz+hbFY5K5c3KIFdO3quFxRCotGjbR7xh1hYTSKdlWuLK4Pd65zW2Hh4aFZfcqKO5RqXaxSxX66u8LCldWgJGIMXAVvBwdDz56iLodt8DbYV99OTaXy7t3i/7vusl/OHVeovn1hzhzx3dbiA84F6/798PLLQkSvXet6vTdKaVssclusy/u7VnLTIIWFpGCYTPZFe9TOUnnl5EnxoggI0Mz9aifJ4pJkHjiQrKAgMU0KC/GZX9vU0bYqVUTnQu1AZ2Q4t1jcDMIid6pZ0I6n6r4UGqp14gpjsVBrrqi+/jodfPqpOP5RUVrHrSg6qrbnUe0AuuMKdeKEFmB+5oxWjTwvbIUFaJaYshDAbZs9qLAWC1dWg5LIiuRq2zqdqIS9dq29exLYPQd0P/2EITsbpU4daNnSfjl3XKG2b9e+N2liPy+3xcJkgvHj7dtYXNg+w3Q67XkohYVEkidSWEgKxpEj9u5AZeHFfiPYdo7Ul5QqLCxuJUqrVpjUDkJxu0LZvmT/C8JCdaFQj+nVq9oLs0EDbRs3kyuUsxgL9XiFhopgUSicxUIV/ZaUp5vObuJKvWpCcGzdqq37Rjuq2dn2RSUL4gqlWghV3Omo5RYWlSuLz7Jgsbh+XRNUuV2GbsQVCkrWYpF72+C6425juTR89ZVYzSOPOC7vjiuUGp9zzz3Qp4/9PNvrKiFBFB1cv16bX5zPSDUltmqFUgdHSkpY5C4iKoWFpJxQboTFtGnTaNu2LYGBgYSHhzNkyBCO2eZ4lpQMuQtilXeLRe74CtA6wZaXoZ2wKM6Rw+vX7fOWlyFhkW3KJi41zj1hYTuCq3a01A6C+rL28RGd3JvJYpFXjIV6vCpV0jJlFcZiYdMBn71nNt3mduPxFY8LEVe1atFl2Tl5UlwngYFQtSo/Bp5lVW2KR1hkZWlitFYt8RkaKj6L08feXdS2VaxoH08EN+4KdaMWi/37hSvc998XfNt5oV63mzej27cPk5cX5lGjHJdzxxVKfd68/bbjYIZ6vV69Cm3bwsyZ9vOLs7OtunOq11xJCwv1ugoIEJ9SWEjKCeVGWGzYsIExY8awbds21qxZg9FopE+fPqSVRPpPicaaNfb//5eFBYiXSePGJSMs1Cw7KmVEWCw6uIjIDyOJ/DCSPxN35N+25GStg5lbWNh2wqBsCYvYWFixovjWr2bDUtOlgtZZcSYskpIKVr8gPV2zfNSuzcNLHwbgtyO/acsUVZYdm8xe8/Z9x30BfzLofjBmunF/FFRY2LZVPTbFXfivILhyg4Ibd4UqrMVCUURGu+bNxWDQG284X862w+/MYuEKVVgsXQpAXJs22j1tizuuUHlZSNXrdf164TZXuTL8/DMMHSqmF9czUlEKLiyuX4fffy+6NuUW01JYSMoJ5UZY/Pnnn4waNYrGjRvTvHlz5syZw7lz59itBo1Jih+jUfjcguaS8F9yhVKxFRZNm4K3d8m4Qtm6QUGZERbv/vMuiRliZHjjtb1iXl5tUzu3QUFax0g9pqp5Xx1xLiuuUHFxonbIoEGwZEnRr//CBRFHodeLkVcVZxYL1V3JZCrY9aZ2hCpUYFfmabtZ6TmWDn9RWSz+/ReAA62q8dQfTwFgNMCF7Kv5/za3sHCn2KKKer2URWGRO3AbbtwVSu1YF3Q/582DgQO1/1NS8t4uFMxioe6XZTDkatOmzpdzxxUqL2Fhm3IX4PHHReYp2+Dx4iAhQTtm6kBAfsJi8GAYMgTeeafg2zMa4d13Yc8ebZoqLNR3rRQWknKCkzu5fJBk8T+u6GyUxEJWVhZZNjdjcnIyADk5OeSUZJEbG9Ttltb2bwTd9u14JCWhhIRg7tMHw1dfYYqLw1wa+5Kejn7+fMwDBzp/obuDouBx7Bg6IKdOHesLw+DlZVXc5pYtycnJwWjpGJtSUoptfw3btqEHFL0endmMKSOjVI6tPisLA2DW6UjPSOVg/EHrvPOZwkKlZGdjdNE2XWwsHoASGWldxsPHBx1gjo1FD5hDQjDl5KAHsa2cHExFuK8Fvc8MDz2E3tJBNH/8MSbbTlkRoNu0SRyTZs0wentr15qHhzjnly6hA0whIZg9PfEwGNCZTORcvWpfTyCvbRw7hgdgrlWLZUeX2c07En+EZuHNmFcploTOMPEGr2PDjh1cCoRekatJydY6rWeyr1Aln/V6HD+OrSe+MSMDxeaZ7HDOMjLwBBSdDqPJBCYTeh8fDBTv/egu+nPnxDUcEeFwDavPEmNaGkoe7dTn5Ij9URS7/TEEBorfX7+e5+9zY5g3T1xXzZuj27cPJSfH+f2alYWaSDXHbHbbzcfg6Wk3Knm1WTOn95r1/jYaXd7fHkajeAYrisP2dV5edp2UnLvvhpwc631jSk93ef51y5eDXo8yYIBb+2T3W8u9pFStitFggJwcPDw8RDszM50eJ0+Lm7CyYAHGF18s0Pb0r7+OYdo0lLffxmhJoexx/rx4JkRHi2OYmVlkz8jy3A+5WSntc1aQ7ZZLYaEoCs888wxdunShSe4sEjZMmzaNKVOmOExfvXo1frlHQkqYNbldivLBIy0No59f8WbByIfav/9OEyCuXj2uJyfTEDi/Zw97cxeRKwFafvYZ1f/6i7MrV3Jg9OhCrcMrKYn+WVkoOh1/HDiAcvSoWPfVq6h1Zvd7eXF2zRoaWDp3Z48c4UBx7K+i0HfzZnyApOhoKpw+zcnDhzlaCse2/pEjNADOXrzIb0tmkGPWHigHL4oYECUri5Uu2lZ1wwbaAFe9vNhiWaZrZiYVgav79hEOxGVns3PlSuqcOEFj4PzZs+wphn115z4zZGQwYNUq6//6jRvZMH06ybYuSzdI44ULqQPEREay32Y/W8bHUx3QWVyejl69ysk//qCfnx/eKSlsWraMlNxVj11Qe8UKmgAX/fxYs99+v39a+xPbfLYxNnwr9IZmRzeTvbJx4XZGURiwbRsv94F4cwo1fGrgl5zKEa8E9l2PISmP8+iRns5ASxaozJAQfK5dY+vGjSTa1KPIfc58rl6lL2A2GKzXXL2LF2kIxB45wr5SuEdsabZtGzWBE+npDvdrp5QUwoC927dzIXf8hQ1NTp6kNnAqJoYjNutolZZGFHB061ZOqZmw8sGQlUV/Syd3891303XfPnSJifyxbBlKLouIPiuLwZbvq9auxWSbGjoPOly/jiV8nsyQEFKrVHF6r0UfPkxz4NLFi+xydp7MZm63XPtr/v6bHDX7ngWPtDS6REfjHxfHxU6d2HPiBJw4QfPLl4kGjh88yPHc61UUKh04QOfXXgNg+aJFmsXZTapu3EgbICE4mH8s6++Vk4M/sHXDBq45yWR2u+XT9rnnLv2/+AIDoEtJsV7jgy5exAAcSk2lGZAcH8+GIr7WC9oPkZQ+pXXO0gtg5S6XwmLs2LHs37+fzbkDiXPx4osv8swzz1j/T05OJioqij59+hCU6wFWUuTk5LBmzRp69+6Np23RHWeYTOjWrYPgYAx33IH54Ycxf/FFyTTUCXpLzvDwLl0Iq10b5s8nytubKoUYEboRdFu24PHXXwBEBwQQVdjtq2bnypXpf/vt1sn65cvh778BaDJyJPWaNSPm55/F9sLCrNvTrV+P/q23wGTC9OmnotJxIdHt2oXH9esofn4E9uoFM2dSp3p1apXwsQXQW1yyqteqhW8tXzgOBp0Bk2Iiw1eIDL3JxIB+/Zy6T+gtAi20cWMGWNpv+OQTOHaMMIvrQkSjRgwYMAC9xS2mWmQkkUW4rwW5z3R//43ebEaJikLp0AH9zz/TffFiTGvWFJmQN7z1FgBRd99NNZv91C9fDpZrGaB+ly7UGzAAj7AwSEmhW/PmKB07urUNvaXIWGTnzlwx/Cq+B0RyKfUSATUC2J6oudplRnoyyNKOjJwMFhxcwNYLW0nLTuORlo9wa81bXW/oxAkOB6bznSWj7YJ7FzBr0XMcydhCUgWd9Zw7Q2d5hihVquBdoQJcu0bHNm1QevZ0fc5iYsT+eXlZ160/dgwWLqR6aChVS+EescXwzTcA1Ona1eF+NXz9NRw4QItGjWieRzv1ls5C7Xr1qGl7ffz5J2zcSMPISOq7uZ+6NWswGI0o1arRYcIEUfMB6N++vZamVyU11fq1b//+jq5HLjB8+63VHc6je3fQ6Zzea3qL62NkWJjz68LGlal3v36aG6Atd92FAkRa/gD0q1bBmjXUq1GDOrbrjY3Fo3NndDYd/75t2jhWDs8HvSUjYMXWra3t9ggKgsuX6dS2LUqXLvY/sBnNDW3WLM97wAFFwdPGVW3AgAGQkYHBcmwa3XorzJpFsK9vwdabBwXqh0jKBKV9zlSPH3cod8LiqaeeYunSpWzcuJFqatEyF3h7e+PtZKTC09Oz1G8mt9rwwQd2lZ8NM2dimDGjmFuWBxafT0N0NEREAKBPSEBfksfSaIRx46z/6k2mwm/f8vLRVatmfy7Ul6unJx4tW6Lo9Zgso436zExte1OmWKuQ6+fNg88+K1w7ABYvFm0ZNAiDJf2iwWTCUBrXqWUE0eDlxd74vQDcVv82Fh9dzMV0Lb2nJ9hXpFWxxN3oq1bVjpXlmOos7kb6sDAxzxJjoDebi+U6cus+s9QO0XXujG7aNFi+HP3GjehXrNCCRG+EkyeFL7pej0efPvbHLNfzySM8XMy3ZILxyMpyfoydYckIlVmnBqcuiO93NrqTz3d8zm/HfuPA5QNak8xX8PT05Gr6VYYuGsrmc9ogzZmkM/Sr18/lZrL+2cA7XUDRwd2N76ZTjU6s84mEDDjrkZr38f79dwB0AwfCzp1iHxXFbh8dzplF3Ok8PLTploEhfUZGyT5/nGFJu2to0MDxfrU8NzyMxrzPo809Z7cOi6uvISXF/WeBZVRb17s3nr6+Yh2JiXhev+4YYG4zMODp4+P+tWZjfdHVqSN+7+xeU+9vcH6ebDrknr6+Bd6+wzPys88c6qJ4pqa6v16Vs2cB0Nepo7Xbsi+5r1dAy3YH6ENDC3ZNHjmifY+KEsdQTYqi1+NhSYChy87Gc+dOuP9++OQTEc9xg5SFvpCkYJTWOSvINstN8LaiKIwdO5bffvuNv/76i5pF6KZQZvnhh9JugT1qEa/q1bWRr5LOCvX221oRMLix1H9qIHHu0Sz1pdmkibXj55AV6to1URdARc3pXxgURWQ6AVG51t1aEcWFmsHFw4M9ccKqM7iecJhIzk4m1dI8l+3LXcMCtCBUtTZDWcgKpSgwbRq8+qr4v1MniI4W+fRBy3x0o3z3nfjs3dsxHkg91ypqjYvCHBdLew9W80JBISIggs5RnQHYG7cXk6Kt6xgJ/HL4F6I/iWbzuc0EewdzT2Ox38euHkNxkY3qh6Vv4XPxSX60xOq+1EUMfNTwE+c6xisNo9kxEN9kNnEs/jDxf/xCsje0r7+RBr2OMrsl+d/D6nzb4N6yErydlaXVDrFNAKFyo+lm1RF8d1MP79sHX38tvt99t/hUr6mrTgLrbzR4G8Q944r8gredBeYXZPu2z6Br18BiPeLdd7UEEdeuub9eFYuVzGkGN2fX65kz2veCvpMslkZAs5Cqba5QQXsfZWWJZ8jZs0Uz4JEf166VflINSbmk3AiLMWPG8MMPP7BgwQICAwOJi4sjLi6OjOIsHFSaHD6sZSwqK6ijMlFRWsewJPPIv/8+TJ4svqum6KIQFrktX+q+tW9vneSQFWrNGvuXZe5MNwXh1CnxIvPygv79S19Y2GRpOXtdjNy1imxFoFcgABcCLcu5ap8qQG2Pa27/8txZoUpDWCxaZGcRpFMn8al2Wovi+JvNIkMPgLM8/0UlLK5ft2Yn2h8ortFmlZvRKaoTfp7CWhTiE8JHPkMA2O0RzxMrniAtJ40WES1YP2o984bMQ6/Tk5aTxuU054XnNm3/yfq9d/StNI8Q/lDRgUKc/1UxiaofVWXd6XVWcbLu9DoiP4ykwVeNqfxQAsEvwo7UYxwLyOTh2+Fyej6DE86yBpUVYXHihDhHQUHFkxWqoMLipZdEe+64A/pZrE5q3RRnwsL2+ipMullAcUdYuLqObacXRFjYFOizMnOmuB6aNoXnnrMWiSzUO0otvGg7OOKusCho9qbfbNJBq4UWVWEREmJ/DRVnunNbjh4Vz6L77iuZ7Un+U5QbV6ivLNU9e/ToYTd9zpw5jHL2wi7v2D5sVEopLgQQDzw1tWz16trDuqQ6hAcPap3AqVOFuNm8uXiExejR4oU4fLh1kjG3xUINohs+HObPF8IgK8vtDD52qCmTmzcXHaYyIiwyPeCKpdNXLagaVYOqcvTqUS4E66ifoLhunzqCa3GRADSLhUpui0VJjYwlJ8NTT4mOnO1I4YMPQosW4rt6/Isi+8aGDUJoBQeDTRyPFVfCwp38/7ao1pWoKP6KE5a01pGtiQqO4uzTZ9lxYQf1QuuRtHAOZC5hr891SId6ofXYOXonHnrxKqgeXJ2Y6zGcSDhBRECEw2aSzGIgJ1IJYN4wrehajUDN6hefFk+v73sR5hfG4HqD+fXIryRlJeGreJChczzPFzMuWwOBnVKWhYVtumpn8TiFrGMxbpwwzK5+LAQvcE9YXLsGq1eL75aYHkC7ppxZlwtrsbBxi1Cio7V7Pjf5Fcizve8LI2zUZ1B2tuaK+uyz4lyoFb0LY7FQ33W2MSnuCouCPLfj4qzutIB2nbgSFiXFa6+Jc/bLLyW3Tcl/hnJjsVAUxenff1JUAByw+EP36aNNS00tWMGsokTthKs+uyXtwvLss+IldNtt8MILha+CmpCgveTyslg895zdCKSdK9TVq/CTZeT20UdF9WGzWasjUFAsQZC0bi0+y4iwuOghRJSPhw8VfStSNVD4Z18IsXTwnLUvJUUb7VPzr4OjsLBYLK6QzmV/Su46WrBAuCb98IM4j40aieJjs2dr13RRHv+5c8Xnvfc6HgOw99XW67URancqFttyUKQEzmjSgGXHRKrZIQ2GAFDJrxID6g6gTsU61AuMtvvZGz3esIoKgLoV6wJwMtF5RzFJJzo3U7mVyEBtNLdqcDUqWjR3mF8YOnRcSb/C7L2zScpKolNUJ66ta8v5D+G5gL7MGzKPZmkijiQ+K58R5fIiLJxRCFeo1FT4/HOhSbdciBbT3REWy5aJY9W4sai0rpKXK1Rhqm6DEOgqNWq4Xs5dVyiDoWCJEnK7Qi1eLCx2Vapoo+yFFRYmk3asbIWFev0VpcXi99/FO121jOS2WFSs6PwaUi2+xUXumkoSSQEoN8LipkPt9D76qOZmYzbfeMXcwqK6QVWvLl4A7hQ+KioSE8GSTYYPPxTbL4yw2LZNvCh69BAvanWf8kkCALlcob78UpyHVq2ga1fN5F5YdyjVYlHGhMV5g+i0BR6YyMCBOrwvC3/9CxUs595Z+ywBxHaF3sDRFapiRR7+/WHCLz5N5EQ47JVUlHvgGlXEAXz/vYiTyd029dq60eOflQW/iuxMPPCA82VsLRYVK2r3VUGFu8Vi8UczX9Jy0qgRXIO2Vdo6LBYYEEoFyyOkf53+3N34brv5dSoKK5NLYYHo3ATr7UWSh38gG+bCzl9DiX8unvSX03mt22tEV4hmfPvxLBu8EO9tO6maAu8N+4qRzUcSZhT3VHx2Ph0/tfNpK8L+q8LCYGDvXm1yormC+OKOsFCt3HfcYT9ddYVyZrFwVfE7P9SigJC3lTY/y1texfHyIrcrlGp9HDFCm1dYd92EBG0ATxVloF1/ua2rimIfb1eQ54aamVCtm5OVJdbnzGJh6wblZurhQmE2a+6sEkkhkMKirGI7mm478muTHrBEUR80aqBzSVosVq0SD7vGjTX3msIIi6++EuvZtEm8gFxZLJygZoUiLU2MboOwauh0hRcWJpNw71KrmRdAWOy+uJuxK8cybdM0l4G2hcby4rygT4N/nuXKojf54w9Y9fKrENeMNTXNrtvnzA0KHEbrY70zmbN3DiCyC+33vl6Ue+Aatdf200/iGnDmXlhUrlD79onrpVIlUdXbGbbCwnbkt6D3lyWhwZJQ0Xm8o+Ed6JyNAPv6MncJPH+iMr/e/avDMqrF4kSi82QEyTpxzoP1udKS+vjQJB7axIr2+nj4MKXnFM6MP8Mn/T6h4o4D4rqKjrYGxIabxD11Jft63vtWli0WatIGWwuBLYWIsVDHGQBik4PFl/yEhaKI5xqI6vG2uBO8XVCLhVoVOj/ctVgUVFjktlhY0oNzyy3aMoW1WKhuUKGh9u1y9c45dkwL9oaCWSzU56Vt5fLsbE0M2QoLW4rTLdo2tlNmjJIUgnITY3FTYTJpI0LVqomHs7+/eImmphbvaIUrbDNCQckKCzWewbYackGFRXq6fdzKihXi02BwTMHoBKvFQs145OEBgy2lpdROdEFdodauFVmJQHQwGzfWvoNLYXE++Txd5nQh0yjM5gPrDaRZ5cLX0HBAtVjoUmDnk4CasdID3YnB/NV1P/srQ7MbEBbzL/yBgiaIkvUlUE3UaNRcDNV4CmcUlcXIksaWdu1cu3nYCgvbY1aQ++viRdi0CQVYpwiL0YC6LvLd+/lx+zG4XRcCno6uWarFwpWwSNKLYxJkyCUs1PN7/bqwWtkOhoDmq92/v3VSuFn8Jt6Yj7UqP2GhKKVXONRVZjmVAlgsFGD29ia8+Ls2OTbRsp8pKeI4uOqAx8SIzqinp2M9nXwsFlcJZYUyjPuyHUN+XFK1quu4ClvcjbG4EYvFmTMiU5KHB3TurC1TWIuFKiwq54r8cfXO+eMP+/8LIixUC6/67Fd/78xiYUtxWrMXLNC+G42le39JyiXSYlEWuXxZ3NAGg7VehJrXHptCOiXKrl3iUx2ZK6gPeGFRFJGBCcC2OFBBhcXy5UKU1axp34Hr3t2tgGtj7jduu3Za50ate1HQ4DpbIfL669rLMp+O7azds6yiAuCfc/84Xa7QWF7250zpkCSEZO/eYlY9f+FeM7O1i/Y5ERYpWSlMZRN7LJdyarAv3x74zu5nKboSEBbHjgkf5oAAx46vLTfiChUTo90Tqp+yTXYxl9uCwguL778Hs5ljvVtyMSMeb4M3naI6OV9WvVZdZJdRBeqBywdIznIsiJRkEYC5XaHshGODBpqoAnFfWOq0WFP5AuGKaMsNCQuTqfRcBrOyNCuAs4xQUCBhsZxBPPJjLztv19grNs+mvApUqWaOZs0cn2f5WCwm8R6jsmdy7715N9GOWbNg2DD78+yM4naFys7WrBXt22vvSbhxi0XuATxX7xzVDUvNVOju9ZiVpQ3Y2brSZWbmLyyKyyU6NVVLVwzi/VsUSSwkNxVSWJRF1FGwyEitg6E+MEvDFcpsFhmYQMQUgPbCUJTiDSi/dEkILb1edOZVCiosVKvHnXfaB8Q7y9TjBFNuP/zu3bXvhbXeWIow8dRTdmlPsz10XArA6Qsq25TNrH9nAdCwUkMAtpzfUrDt5oflZX8i2QcUDzy8jDQUmyLKW3Q8/4ly3j5bYZFtymbxkcXU+6IeL2et5Jm+oAD33efJycSTVPKrxP0VxPWUoi/CzmF2tvPrQhXHzZvn7fpRWFeodeuEcO3eXfxWFRa2160Nvx/9nY4J77FIHawsrLCYPx+AvwaKzkmnqE74OrFGAJoAcNExqVGhBnUq1sGkmFgfs95unslsIkUVFgZ/+x/a3h9Go8ispm5j9WpRv6RKFa3zBYQhhMUVUz6DJXnVsYDSc4dSrZfe3q6Dad0UFiYTvMA7DtNjLxg0MZiXO5R6bavulLYEWvJDO3t3mEzMQ8T/LF5cgEd53boifqitYxyPHQUJ3i4Itq5Q6r7nroZdhMLCZDaxpkICib7YPxcUxVro0fpecXeASR2E8PcXA4i2VhhbYeFMdGVmOk4rCn7+WWzb1gL3X03pLyk2pLAoizjz/c/r5VDcHD0qzMm+viJgGexfBMXpDrVvn/isX99+VLQgwiIlRbN69OkDPXtq83IJi9hYeP55Eb9ni9HXF8W282Sb9riwgezqaJWNb72iKPQ7N5WoZ+CYl+MI5Tub3+FS6iUiAyJ5v/f7QPFZLM4mCv/u8CoZVq8C7xzhHnCgMqRnOBlBVTNCVanC5PWTGfbTMOJSRSXc9TXheCgsj0jGU+/J8vuWU81LuGkk64so3ayiQPv2eFSvToRtZhOzWUtH2a1b3usorCuUJTMTmzfDk09q/vdOhMX7/7zPkEVD2JZ9iq/bWCbaCgt3rymzWVhigDVBYkT61pq3uv5pPhYLgN61hHlq7em1dtNTsjUBEOwRYDfPIePV/v0iruT4cVEvBETxR5vnRjiWrFDmfISFs1FtT0/tGVBawkKNM6hSxbWriJvCYl9iFIdpTKBPNjNmiCR4YMkv4U4tC9Vi0aaN4zz13DjrjJpM9GC99d+dO4WHZnw8LFyohW0UmpJwhVJTLTdpYr/MjbpCWYRFjimHQQsH0af6Bsb3w/6dc+GC6IgbDNCypZjm7nNDdYOqU0dcP7bXim1WKNt5KsUlLLZtE5/Dh2vXdHFtS/KfRQqLsogzv93SdIVS3y4dOmgP9JISFmqV7ebN7ae7Kyw+/1wEul28KEZVu3QRLlWdOomCZblSJTZqBO+9By++aL8axcMD09KlQlD06WPfOb1Ri4UatwIsPLiQv1P2Y9LDpqDrdotvP7+dtze9DcCHfT6ka42u6HV6zlw/w6WUSwXbdl5YXvZxCcKFonoNs3XwLyvVl4hMD0x62Hv9qONvLS/Eq37w2XbRka8XWs86e6WIDaZz9c60r9aeQE8x8pxiKCJhYTTC3r3oEhJoP22aZkFZskRkhAoIgAkT8l5HYYWF7XCvWgG4Th2tg2Ph96O/M2ntJOv/F9Q4zMJYLOLiIDubFF89f17cAIj4isWLxSD6hAm5RqFthYWL4eletXoBsOb0GrvpSZnCZcnbCN4euTo6np6iM9Krl0i2EB4u7t3Gja0WFVs3KIBwgxgsiVfyGSxx1fksjQBusxnGj4dXXtGERV4xWm4Ki+tZovNfvWIajz6qCYuLF8EYbLGGuBIWiqJlF3JmsVAHRJx1EM1mFDRRNGqUcHusXBnuv18YeG+IknCFcpWZq4gsFuP/HM+fJ4W70981sX/nqANfDRqgBAZy1Q+ULDc74uqzSXXLtD1PthYLcF9YGI03VrRWvY5atdLaIy0WkgIihUVZxJnForRcoYxGrZOkukGBvStJccZZqA/uwgqLNTadozZtxMPSz08UJZozx25RNTYetMFnW5QePYQ/76pV9q4fN2qxsAgLs2Lmlb9esc6O89BeHrFJsQxcMJBsUza31b+Ne5vcS5B3EA0qiZiXfZf3FWzbeWE0YtZBUqKwTtSpbbAZNNXRNll0CHckOakMb+n8fHpxMWk5abSKbMXRMUcJsmQRmm0Z1OsZLaxGgR4WYVFUFotcHRj975ZI2B9/FJ9jxmjBrK4obI0UZ0X+csVXJKQn8OjyRwHoWl3cT8lqn6FyZTKNmby07iUeit6HUU/+wsIiTpe3DyHTmEndinXxud6C++8Xp+KTT+Ddd22WV0evFdcFDntE9wDg6NWjJKRrprukLCEsgrJw7rryww/ifuvTR3RQunfXjknFig6ZscL0FmFBPsKgLAmLbduE5evtt7WHRBEIi9QcsVyAj9jXypXFZWg2wyU/S8fTlbC4fFmYWPV652lv8+ogmkykobmVHTliPzs+/gYHrPN7NqrXd2GzQp0/L2JHdDrHzFyqoL9+vWDPZhth8evhX/lq11fWWR5m7O9zdeCrWTNej/2OsEnwR5ibqbNzCwtnFouCCos+fYRblTpoVRBsk1u0bCmFRVlgwQIxOFrOkMKiLKIG9ZYFV6iPPxY+rBUqiJoaKiXtClVIYfF3alt68DdHaABPP+0w/9Qp4bliNgu9oOIqFtMphbFY5ORomb8sVpNNZzdx5rpWaCnWR+uMfL7jcxIyEmgR0YL5w+Zb04SqwuJ4QiFraDjDaCTRF5Rr0QA0rOtj543RLlW4SO1MzbXNjAzIyiJHD9+cFAUEX+j8AjqdjloGMep60JJoRRUWQRaLRbJH3sLiudXPMXTRUNKy8+lE5joHuuXLxcldv15MyJ2K04YraVf499K/hbdYOLsWc7lBvb7+deLT4mkU1ojvhooA9it+YNaBSTFz63e3Mm3zNOaEnhNxLPldUxZxOr+JsD7c3fhuPv1UZ9fvUD2RAM1iAS7doSr6VrSmnd15cad1uhrMHZxJ/j7xVaqIG0r1O3/iCQd3oXCDMNWk63LyPq/O6lhA6QiLpUu17xuEhahIhEW2uOb8LcJCr9dWG+uVj7BQBU6dOo41WSBvVyizmVQCHCbfdZf2PXcyKUURz0234jGK2xVKbVytWvbXNmidcrM578D33NgIi3f+EXEvar2XeH9QcmyeCxZhkdysPm+eEmnIZ9V2U1ionf9atcSnrcXCNt0sOBcWuU9AZqYY+MrJ0VKYF4SjR8U6AgNFm/K6biTFz759wgo8bpywTJcjpLAoa8TFiQxGYD/CV1quUAsXis9p0+xfoCUhLDIzrf7jDikU3RQW/Ta8wAZ60MVvDy//e4dVp4Clk9xOGGKqVRPxpirOMjO6xB2LhckkOt5r14rlLlwQn15eVpP79/u/t/vJOV/xAssyZllrPkzuPpkAL60jUK+icDMqamERFwBcFP7adWt72HkVNMoSHcLTWbkedpaOz/L6OuLSLxPuH87tDUQMS61IbSTV18OXdlVFhzvQSwjmFIPra2jt6bV8sPUDlhxdwst/vZx323NbLP75R1yrV66IjoeLQOrZe2ZT89OatJ7ZmlcTfsako2iEhY3FItOYyQ/7fwDg474fWyuZm/WQuGUdCw8uZEusFoi/vZrj/jhw9izbqsGKSonodXqGNx1hTVLz3nvi07YoMJ6eWifOtkp6LtTzs+OClvUnKTMJ0kLJ+vdJkrKcdGBz4+0tqkGvXg2vvuowO9DTH29Lv/JKeh43XFmxWCiKlt0KYONG8ZnXKISbwiLNKDrKqsUCNE/YczqLu2aSiw6rGmNgm7LUFrXDajQ6WtVsLBZvvy0ujzfeEGVe1N3K/SycMEFomB9+cL0/RqPlcXCDdSz2XNrjNDuZQ0fbmaXG21vrHOfnDmU2w8SJ8Nhj1lH7f4PT2XVxF556T97tJcx+GZ6QlmMjyPftY1cV6KTXrN9haW5GwOdO4a7u07Vr2rNHffA6qyeR+5pSg9hByyZZEFQ3KDW5RT6JHiTFiKKIYE+V0qpfVkiksChrfP65eGB07Cj+VErDFSojQzON2qZ6hZJxhTp0SHSsQkMdX95uCotss1guMd2HqVNF+QK1+PLnn2sDQ5cu2bumFkhY5GexGDtWZPhq1Uo4MC9YYP9S0etJzEjkp0NilP+leo8AEOsnXrrf7fuOq+lXqRJYhYH1BtqtWo1fOJZwrAANzgejkUdTl0CCsIbUrGkfPxphFtfiJWOul7Xl5f11B3E8HmzxIF4G0WGqVVUrADWw3kCrj36gp1hXiofjNZSanUqv73rR+/ve1mmfbf+MEwnOaywAdufgWu5aGi1bOk3Ufy7pHI8vf5y0HNFBfevyz9QeX4iifeq1qLpgeHraWdqWH19OUlYS1YKq0atWLzwNnlT0FcteqhnGGxveED+3TNteFbeExcuWmmCjmo9Cn9iAs2fFbo4aJaYnJeXqV6kdhqeeEtfl9/aCFlwIi6wk2PY0sf9Mp/HrWnXnU6fy6Ld5eYlr3knKTJ2nF1Us4yR5CmOjkc94ik/ODbOfXtLC4vhx50Uwi8QVSnT+/X20+0AVFrFGSyfR1X66KyzAcfTZZLJaLIYMEUasVyzemM7KXxw9Cp9+Kr7//LOrvRHhNNWqwb4zQdbtOCUPYfHdvu9oNbMVT/3xlOPvct/HriqfB1sKDLoSZSoffwwffggzZ0JqKol1qzE+RqRdHdZwGNEVovFTRBut6ZHT0sg4dZTb7oNDGZrr0RVvNwfacgsL9TyprtBeXnl7KuQ+l1tssgMWJkXsP5YkIGqClrxicyTFy/ff27tQlFZK7UIihUVZwmyGuXPF92eftXcdKA1XqL17xYO/cmXHAlAlYbGwdYPKnXXFTWER7OF4vB5/XGimjz8W/3/7rXCH+vtvbdCmSC0W06eLFR61BDvPnKlVarW8VKZtmkZKdgpNw5syvJYY5T8XYOREwgmeWf0MABM6TMBDb3kBL10Kv/5K/dCit1hk5hjYmi7aUKnVZlq21ISFyQRBJhHUHWdKsq/6fe0aB8NhdQ0jOvTcU/txq7U+ukK0dbF7G2sJ84MsFotkD/trSFEUJvw5gXVnhEk/zC+MOhXroKCwN26v68bbXIvbX34Z4+LF2qjfsGGYFTPf7fuOZceWkZSZxK6Lu7jjpzvIMefQI7oHXw8UnYmzFeDHSCd5//NCvRYHDRKuBCNH2nXqvt3zLQDDmw5Hr9Nb9wtg5YmVnEg8QbB3MN8NES5S26uRr2hPPX+a9dHi+yvdXrFaK7p2FR1DNWOmndVCdRlZulSMjI0caUk/pGErLNRznJSZBOdESs8L1/wxGMRPGzYUI9hqQhm38fSkp6VdanCsM5JSdIznMyYcHm03KFviwsKVe0kRCIs0oyXGwtfRYhGbZfEfLA5hYeMK5e8v+vfqo9ZWWGRnC88vWxcp9ZWUG6NR1CJNS4MR7zS2bsflwuBUWDywRKTB/W7fdw7zHISFqxF6d4TF9et26b4BHr83gM2xm/HXV+QW3iQzE8Itlp14k8WCsncvM1opXAoEvU7P7dWF298VHzfeh6mp2khWbouFei+GhWFG4eOtH3PHLVe4rp5G9X1TlMLCbAY1Hk0dRJQWi9IhIUEMRtoihYWk0OzaJfzuAwIcfcFLwxVKzc/dtq1jx97WYlESwiI3qrBQK4O6oIaPo6vHzp3w0UdilLVGDXjgAVGwtUcP7f2UkFCA3SpojEVkpJZqsHZtEtIT+HyHCNB6t9e71LB0wlO8YMCCAaRmp9IjugcTOliyGcXEiOHFO++k3rsisP5c0jkycormBZCapZndb33+KwyWVPrqIfc2io5OFkZrQC8A16/zqcXzp+npb2hVK5r69UWs65E17SFJxAzZVoUOtLh1pXhqHY/U7FRu+/E2vtkj9u3z/p9zZMwROlQTroGnr+VR4dzmHGRVqIAycKBwp/v2Wxgzhrl75/LAkge47cfbqPBuBdrOasuui6K3Orn7ZB5r8xhv1xQWozM+BRypU1/mERHi/KpJDxAd9D9P/olep+fhlg9bp4f727vB9a7dm+7R3dErOi4EwYWcvDO87Eg9hlkP1b3CqRlS0zrI1a+f+KxZU3zaCQtVJdryzDN2/7aIaIGXwYsr6VesVbiTspIg+Jx1GbNZDKzl5Ig+0p13FrCkjacn/S3xqytPrHS52JVr2vXYti2MGCGMLZk+lv0oaWGhphUF0Wl39nxScddiYREWTi0WGZYCd872U1Fcp1tVMRi0mzdXZ9SYZSIL0WO1rS0HmrD46y+hWXr0sE9q4azeHmjjJwAHzwTwFY+jmAomLPIcPABHC5gqIHLjjrA4cUJ03Pz8ICyMDG89y71iALg78V8eu6suFSrApc/2wr8PWtMjJ+3cxNSuwJYJ1FkaS3+vF2DtVOL0QfnfCKp4CA6GoCAyjZl8UCeevRHavJzwStzx0x08s/oZfmsIy9Xkes4sCWazZnGAgndEt20TbtjBwVo6dmmxKB1+/VX085o00eJsy1mRQiksyhJLlojPAQMcH5wlbbFIT4c//hDfXfilFzobkru4IyzAeTYeCzlm+yBTNZZUNfcPH25vfFGL1CqKYy0Ll+R3HHKltOX8eTth8fPhn8kyZdG8cnP61emHv38FKlrceE8mnqSib0W+H/o9Br2loQsWWF9coV/OJcRLvDzVDuCNkpZtObYeGUQGiY6vTqf1RzPNlUQAL1hrVAAoiYkssSRmUY4MFW06IbJzTn+lDdFbljF/2Hy74m2B3sJVwtYV6pW/XmH58eV46j35uO/HjG03llC/UGpWEL1kd4SFotfbD70+9BB4ezNv3zwAQnyEFcPXw5eBdQfyRf8v6B4tih42CBLBlGf8ChljkcsfOjkrmfF/jgfgf83+R93QutZ5qrA4dEV0DvvU6kOAVwD1c8RxOWS6mOcmtxguQkJtKh97mZ9+0mLU+/YVn6qwUA1kgP1oWNeu4gb45RfRg7Tg4+FDx2rCFfPvM6KycVJmEuQIa8fgVheoX9++LRcu5O9xYoeXF71PgUHRcSzhmMvzejXJ/njOnw9ffAHLrnUWE0pCWJjNWoXnxx7Tpt92m0hn7Qq3LRaWzr2fuA/i0+LxrijurdhUi8XN2X5eviwOul4vita5wkWGn7R0bcDIlbCYM0ckMAoKgltu0VyhnFl1ExJESI0tT/IVq1M6Oi4MLoXF6+tft/s/y5jr+OW2WOQ6B+k56QxdNJT7255DgbwvTNX1qGlT2LaNDb99TEaKL2EJt3Ngs7AmZGVB1rVo2DuKeCWVK2lXeODCdK54BKD76y2O767C4yN6wuYXOb//hfw7grncoO779T6eq3WSJwdiFRZj2yew5OgS609iKli+OOvw79tnr/QK2hFVY4cGDtSOrbRYOMdsFpXnbYVcUfKTcIlmxAjtXEuLhaTQLFsmPocMcZxXkhYLo1EMT6l+FZ06OV+usPUb3EFR3BcWedx0KUb7wl3/+5/9/OHD7f/38NBc5N12h8rnOCQoFdmFTX752Fi7CtXzD4g8/yOajRDZnry8NLM3MPu22VQLsoxcKIqdT7wOqB8QDTh3hzoUf8guZag7qKkv8UolIkBzMbDGWSjBRFj0ra2wOJxwlKv+4GP05PRhIXaeeEJbr19KC+5ver/dtoIswiLLAzJyMvh468dW683S+5bydIenrcvWChEd/tPX3bBYOMladPb6WTae3YgOHfuf2E/cs3HEPxfP8vuXM6bdGOtyNYPEy/50QAFfzk6ERVxqHD3m9mDb+W0EeAXwWvfX7H6iCguVPrWF8g1VxAWQbMrjpX79OpsrmWDeX+ycN4577hF9gCpVtMFrW4vFunWW7MtjxohnjJeXCDR6/HGx0Acf2K3+lpoieOOvGCE4krKSwHI/DWl3kc2bhWvM7NnawHCBkpd4ehKcBW0yRMd596XdThdLSLa518P3W79uzrb0fEvimbh3rzBxBgbCvZorH3fcYbfYyZNiXEj1aHXfYmGJsfA1oygKzb5qxugNwuwUm2w5uM6EhRrzER3tNI7FiosMP+o4lQGjQ1/dNiuzh4fYt3XrtNdB7udjerp4VKs1OGj3OdRdAcC+zFwqVMVo5Cse57HTz1tvn/Ux61l6bKlwFzQJwWH7nAHytFjkmHIYtWQUS44uYWFYHMdDyVtYqNaDqCioVYuVupOwfAZXPl/Crl1CeN16q2XZtMrsNsTT6MtG/B5wHg7fiWK0z0aVlVaDnPR8BgAtwuLfBsG0+LqFVUBsjQLOnyfFC76pLOqktI4U746zwbCrugfNhiezqjb2Hf7caq6gwkJ91w8erE2T6Wad8+GHIkPmHXcU0ETrBpcvawMYd99d+AyFpYwUFmWFpCTNpG19itlQksHb33wj/IWCg+Hrr8UwlTOKU1gcOSJ8Xz09hRN3bjw9ORQG2QbyfIimmjRh8dtvoiP08MPipXnXXc5j/tQXqpp1MF/ysVi0v/gbbdnFhrcshQYvXoTjx1GAF7NWsPncZnTouLeJpcPi5cVIi6Ya12asNbMSIF4AR4+KjoKlp1/PV/h4H7tqH8Adcz2GZl83o+anNck0um/OVlNf4plG5YDK1ulWYWEOsgoL28J8G5JFp695XCfS0nT4+4t+qxr/7ywBUaC35qj9/NrneWb1M5gVM/c3vZ9+dfrZLasKizPXzuASF8LiUsolhi4SVpTu0d2pFlSNygGV7TJsWbdjsYxc8TWTml2A+y2XsLieeZ3uc7uzJ24PYX5hrH9gvXUfQOh3W2HRKKwRNSoI61aQIjpOyWbXL/Wr+2PZmDgKkqvbTe/eXTPWREeLz7VrhbWuTx+YPUcnzO1Xr4qe4HhhTWHVKjtlYBUWZ/4iy5glhGuOuJ98fRQqVRKDaw8+KMKwoODCAqBupvBdj0mKcb6fKZbrsfYq/MZ3xHDLZAC2Z1pG8l1ktipSVGtO9+7iuTh2LAwdajcIlGPK4d4HE/njD3FMatWCqrfU41k+yN9iYVJjLExcSLnA5bTLECw6vPGpfmThlbewqFfPcZ4tLtxa0iyW0QB9uoO3a7iN5m3QwCbmQhHFLq5cNdv1q5Yu1VlrBgIQudvqOncdx6QJABiNPMlXzIy7zeo5OH3Hl3CtBq2OL0b3Tgqse4uLKbksdy4sFqnZqdzy3S38fFiLLN8SRb4Wi2wDDKn/LwFTA/h8++dwWAsmqVlThMmJDUQwI/AYV9OvEp0Itdc97Lg+n+skJOdzI5wTlpQxdU/Y1SDyzwZiY9lWDcw6hZoVajK2nbAwnq0Ab95i4ECoke+aY38ubes1QcE6ohcvCh83nU4kWlCR6WYd2blTi8e5fFkrzlhUzJ4t+hHt24sLT73OpSuUpFDs3i3Ub3S0/RNdpaRcoeLjNT+ht94SZv/cbxyV4nSFUqv19u3rdCTurW3v0WQMvNkNlzedokCKSYwmXfj+L4YOFav65huxm6rFMTfq4VdH5DIz4aef6rFokYvjkI/AOmWMBmDutgZi6M9kgmvXWFkX3jkh0hSObTdWs0p4efHJn7BpNnzSfZq2opwczRf+ySetASH1PCMBOJ5ob7E4fOUwZsVMSnYKb2x4A7Pi3nlSA0lzWyysKWdNQURaBoltRxI3ZIntV70sKje3bCkOjdrpTEhw9Frz8PLBx3L6VEvFmz3fZO7tcx3apXbKzyadxWh24f7mQlg8seIJ9sTtoZJfJT7o/YGTH2oE+1UkxNKfz1PE5MZGWCiKwqgloziecJyooCj+eegfWlfRrFZLl4qxgmVvj4BMcW8/2OJB6/wgS0csxez8pZ6eDk2G1iRj1SwAJj1vtsZcjhypLafW3jp2TLtNH38cEq7ptWdK3boiA53ZrN13iADuIO8grqZfxedtHxFIbxEWfj7215Iam1SgPr5FWNTMFJ3emOsxThc7oxok/K5y6MlDDO4gzDFnky036qUirDrvClVYqIM+n38uRiosL/7Pv07DNzCL3Zu1KutnzsDFyx58zlMomflYLCwDIP5+CrsvWiw3vol4eotr6jzVikZY5Bp9Tk0Tz/AAg6OAPZujRcqr2b4VRWHAEpGGOitTT1qaWOWPP9Zn5Eh7d6b1L39AiK84x/sCnNc9UXK0+3jsWGjdxsQvD34Jn8awa/5tKDk+cPAeLqXan2PF05NUW21hsVhMWjOJzec2E+QdRKMwMWr0T3XytVi82hN+9zwtMsNdj7abXaOGTWx4VjCK0QcPRc8Tc+/lVGoXvLxyGfUzK3AlOZ9r8tw5lteDbV6X0aFj6b2iPkqaF6QqWVg8sOhSvQs1gsVgw4FwWFlDXA9xAWgd/sxMkX0ENOt+QTqiqihp00ZkYFSRrlD2pKTAfffZv8RU39OiwGiEryzFGFVTv7RYSG6IHZa0jm3bOp+vDhefOeO6UFJR8MQTogfYrJm9L7EzistiYTZrSdJz+y4hOnuvrhcuJV+0w+VDNDMTTIiXXWCQC1HgBNtsKOnp0K6dBwsWNOSxxwx2I3QbYjYwaMEgRl36in2VyVdgnYv3tkubu7KZeHA/0vIRPuv/mbaglxfBWdDlHOhs9+2rr4S1IixM1AWwZMWpbxAdrNyuUNcytByg0zZPo9O3ndwSF7auUJX9nVgsjAFOXaG26S+AAinnROertaUfXbGipkEd3MsMBlHJ2UKdinV4scuLeBoc87ZXCayCl8ELo9nI+eTzzhtvMvFxB3ila451X38/+ju/H/sdg87A+gfW23XwneLlRS3LoZv17yz3g+JthMXGsxv5/djveBm8WHzPYru4CrMZXnhBDGL/u7YOrBU58h9o/oB1mUCLsEhWnG97zx64nCisLXpDJk88ruf338Xl0c/G0NO1q6MnYU6OVoPTyogR4tOmToOXwYsFwxYQGRBpnVY5TYwM+/ra/dra8SqMxaJmurjebItD2hKbJp4zfj5JRFeIpk1joXATEywbLW5hkZ2t1axwYr396iuFcU/4Y8qwWL86fgBj6/PmPGGhzMGLJJN/ns+HNJMlxsLfbE0mgA78QoUbYyxRzoXFCUtclY2wyMiATZtyeWm4coVKE89Ff739dfbXmb+Yuvtp6//qNbQldgt4pYOHMHUsWgQ9enjw448NrMt6DXqWNz++RPe2lahusSRc0zm3WKjbB3F4/t1tgPRKoM/B19eyA+mV7CwWh68cpvV3nak0CeHmBBAczIrjK6xVsn+7+zfeuVUUt/snH4tF7JVTvG8J14kKiqKL59N28195RRhEPD0s93dqZZ7cXoUPkkWwycsvw4oV8Mgjlh9kVuBKSt4K23zxAi9bNOrznZ9nUL1B+FrS2V72x05YqBn14gLBqBPXkJ2w2LFDPEwiIkScCBRMWKhuUGoAoooM3rbnxx9FbGRUlKh5AkUrLJYvF255lSqJfM2gudXeDMJi1KhRbFQftJKiQc3A5CpQunVr4RKUlCQsCcXB+fNiFE6vh+++c16Ux0JqKhj1lpdFUQuL998XPqiBgfY+nxZeXPei9XtYOi4forau1/6B7l/qtq5QCxfC0aPi5ZeerrNmCFQUhcdXPM6KEyuYl7SBdqNhfVDeGXxiL3vZpe1dVVu8OAfXz7WPBoNmJVIfKAkJMHmy+P7WW2KEzpI2tB7i7Xrs6jG79K8JGfaxFdsvbOdU4ql89z/ZxmLhzBXqarbmChWXJnqSZsXMheRwmLeONftEoKbaBzMYtGPqMKJtMBBo88wc23asFqSeC71On28Ad1Z2Bs/0g6kdslmXuI4nVz7JkEVDABjdajSNw12k5LTFy4vqln7I5zs+555f7sn/N8B1UxpPDoRe6V/TY14PQFghcguZRYuEp5+Kd3xnXu76MmH+mlN7kCVTT7JiP9K9d6/oOKppkQk6x8OjHiQ6WhjDcgdUe3mJ91WTJsIlWBV7DgJAVSPbt9tZRQfWG8jJcSdZef9Kfhj6A8EZQsz6+tj7Ft+IK1TNVNGhcmWxuJgmlgvyFe26pZUYwc1Orkw63sUvLLZvF536SpUcMi/t3g1PjROdPUOrucz5/TiTXr8GlY7zRfxd+FuCsa8QJhZ2QarFshrgp9jFmuhCRH2EY9R322LxzDPQrZvIfAeWAWdXrlAZzi0WH279EPy1UYBmzWDnhZ3c9+t9YoKfCBR+5BHYs0eHwWCmWqNY6D6ZgSPO8MrTQoz6WcRBBs4LKl5Psb/XPf1S4dHWPLtsCleuWJ6BWRU4fSmRV/96ld7f96bljJbsOXeULA9LrRdgV/pJRiwW4nhcu3HcWutWOkaJ59DRMLiQ5voa+VN/CkUHtY68yqhr54i6IFwDH31UHPJbbxWP40pB4vqLvFyFxFVvcIVwGjdSeOEF8Wy0JnLMrMCV1LyFxSKv4xyoDMEGf57r/Bw6nY4Is7gGzgXDNovxukv1LlQLqmZNT61iJyw2WVxsu3Yt+Ah3WpoW25k7E2VZs1goijgptoXjShL1/h0+XLhBgjbgUBSosZOjRmn3681ksUhJSaFPnz7UrVuXqVOncsHOsVJSKFSLhSth4eGhldKdNavog4ZAc4Zv0CDPFIrr1omOxOD0H0XGjaIUFps2wYsW4TBtmsPQ6P7L+1l0aJH1/3PBYM527magCgt/UtF7uxZJtuy8sJMNCWL9J07AvHn2889fEPu69vRajl4VeRVb+dYi2wPmRub9MomN87T2wE6FwCm/TDz0HvSM7mm33Acf6ujAVq4TrD1QZs0SwaNNm4ogEbAKizo5wqXlWuY1OzGRmCGEzuOtH7cGAO6/rAW/uiIBVVikWYu1gZZ05uMdnciMF0WUVItFQnoCpkW/Qcwt+HoZ+eQTe02oupc5xK14eGC0eQrZxZM4QR29c9UJtd3/6bHT+WbvN+h1eka3Gs37fd7Pc91WPD1paGNZWXZ8GZvPbc73Zx8bdXy14SLrNveyTnuy7ZN2y/zxh0hvDNoAYSVTM966RQwWZGaKjuF3s36BeWtItLm0N28W7mVdu9oIixbzGFLfRUEBC9WqiVv7l19EpmNw0hevVUv49BqNDi9LP08/+tftz/Bmw0k3ixeeK4tFoVyhLMLibNJZTIrjsyQ+TWyzoq/oWLetWxO8k0HRszG4pthocWWmAy1tcP/+9mm2gacmpGMyGqDBb3zwRRKjbqvHlFtfpWGlhiJOIkBcj1cIEx0GF8/KVLMldsXXxmIBZFYW1902OjgKC5NJSwJhuTnT0kRYHIhB1cqVxWPiuQtPi4kOrlCi824rLNaeXsuqk6vAX7tZI2tfZdDCQcQmWwKdbURH795mvv56DVUn3AU9p9ilk/ZXPbDMzoXFtWRNWFSPzsH4cFuo8i+j2/0Pf3/wCxaWkY///JW3Nr3F2kM7yf72T5iaDh+f4bB3JH/WgY7ze3I98zodqnWw3ueV/CrR2Vscl7cD9zrdPiYTq0OuQWJNTi96gzffFINJIDyD1JIvAFEVhAvMI7915gflQXSYmfWNztr3s8aPZ1bgrYNfcjkPcfFFlHhu3ufxMkP6VmTzZghXhMXr++aQ7gVVvCvRoFIDPA2eVA20r5WS6AdZ6ZYXnK2wcLO+k4pu2TJx0dSqJfz6bSkrwdsmk7DWf/WVeA++917xpbjPC/Wh27KlFrzmdurIfLh2TYwAgb2Xxs0UY/Hrr79y4cIFxo4dy88//0x0dDT9+/fnl19+IaecHYAywdWrWso72xzpuVGHgJOTxV9RowaPu6hiqijCQ+m224SL0J85vfid24vupW42i5QiiiJ6X2PG2M1etEih5605cGwgwxoOQ28W2YTiU5wPk6rCIpCUPK0vKkazkXbftONokDBxL1xoeWbrzOAjfGP2nxQjdR9vE9X1nmr3FI9WEqO91w2O176t/svM0mNs0gKAv5qJl0inqE52AczXr8Nzz8F2pT2LGSqEhaJonZsJEzQXNIsrlF+miaggYQmxdYdSs0GF+oXSvLIQiu4Ii6uWjER673R8PLQOwZgxIsV5arY3my49DUBskuhoXEq9BEliJHntO7sZP94+NEcd0b58Gc6eFZf7rFnw2MuhnLV5e9sW0nOG6m+sbjc3CRn2ViMPvQfzh81n5uCZTgO1neLlxdgd8MoG6Fa9KwDvb8lflKw4PgRSI+GPL6jkWY37mtxHs8rN7JZ5/XXxjrjzTs2dNi5OvCdTU8Xx/fhjuJJQE8704lRsU+tvN2wQn7t3ixg/ACL20KFGZ/f2C01YOLUsqPEDrgrBARlmITqLxBXK8tKslqLHQ+9BjjmHRCd1OxIzxPUR7is6OB4GA77hwjVmU2BtIYZcFVUoJDt2iPCu9q2ymT3fSwygPGVfAVpRYKdFA7QdvoJxHcR8Hw8fZt8+G71OT5qHcO+64lVNBHqqrku5SLMIi3+Ne7iSfgWDTtzjmREitmMrHcVD15Zjx8TzwdcXoqL45x/HGnmqkP8mbiA5eDi6QqWLLoC/QUzff3k//X7oJwSe73Xo+hb3jz3Fm3seJz4tnsZhjdk5eif4acLiscfMXPTZbq3Sbpt0IcBXPAQyzbkuGAvXU8V+1g24yIhvJqOEHuXWmrdSv5IwvVWuatnn69GQGYTfos0QYxmISYrm35xuvN5DPLsH1xvMyvtX4mXwwmwW41ItDoq4rVnh57mQrA2Anrl2hlYzWvHpuqmsranAAS1bnU4n+tS9tPEBACIqCJX/RbYY+BrQ9DwdbbLoWsvDZAZzMPkkz65+FmfEJV9ka0QOKPDTexPZtElogr0f74O/J7PAcsvfGT3AaqlQkzr0qtULT7M4pvFp8eLBoRbGs7VYuNkP06uBhvff7xhLWVaCtz/5RMQU2vYHSlrsGI2w3/LubNGi6N3Af/hB3MtNm2Js1IyxY8U4xF37X2Esn6Nk3QQWC4DQ0FDGjx/Pnj172LFjB3Xq1OF///sfVapUYcKECZxw8QCVOEG1FNSs6bKc6eXLYPTy055exWElclLBNT1dK4q0eLEQ07bvt8lMdri54uJEbGOBctqDcFTduVNEtb77rt2sF1a/wr0PXybxUGtYuJzeWV9QxfJSPJt8ztnaCiws5u21mCeituIdonVU9HVWQ1Xx0jxw6iqbzm7ij5N/YNAZGNd+HMGeooOf5OH4kMn9fD97xzPw889sGzsEgC5RXezm//ij9t2EQTxs/vpL+HYGBooUdCpqhzw93foiPnJF87FRR+8r+lakaWXxtjoQfyDf45CoE2LC28f+YebrKyzRAGkZmpDJyMngUkocZIuOe406jsdaFRarVwuDWFSUWNfM+QGwWgRTNwl3UeDLhurBwvn4bNJZp/MTMkXHNCRTx1t13uL02NNati138fIiMhXe/BumdBQZQA5fyT/7R7aP1in+sNo55g+bT1KSFhIVEyMub71e1GGoXl18N5lEB/C++0SdqoqakYjkLE3YOcuhUNt7DxVr53/cVFQB4NR7qEcP8alaT53gSljYCkdnKAps3aoN8MXFwZV0cd8YcoxWwRif7ZiKLTlDPBOr2NQVCa8mbu5dAU2d71B2NsyYIdL5KAr8+6+44NzMIf3KK+Ja3bHHi4dNs3ih6g8O8W8nTmdizPADfTYzRj1j567SoVoHUdDSMrJ/pYLF3Odi+6kWN5g5Cb8AMKnzJCGyq20D4BgNSLSLVkYbqe7QgespBm67TYh2EAbnihWF9cvHB64bA/mHznm4QonpPx/6GZNiomd0T3pE94BbX8Wn11R+PfIrBp2B+cPmUz/U3t/usO9M3j79NgoK9zS+R0tCAQT4ifW7FhbCWuXrk2ItiPlEGy1HdVQNy6BVQl0Cf9lMekwTQkOhoiV7xLYKEeyoJuKBXmk6m1s6hTB8uBiXeuklmD6/L7XORGHUKxy5qj0bl26by564PTy99TWu+4B+v8g7PneueG9dvqylalaJqCiExTXEDfrg4/ZJRTRhIb5sOLvB6T4v27MIRQf1dvQm0ab4Y1ZaJdjwOlmWGPi7mwuxM2UK7H9hGSRVY1y7cVS2xOPEZVwRYjUlRbwbmjYtmE++oqDbulV8v92JpdiZK5TJVOQiPk9ycmDqVMfpuUV2cXP8uLh3/P2hTh375CA3MLB6/Tr8ujCLrGkfoQC7+r/KokXisTVvHvxyqQvTGcvpi84tfmWVGw7evnTpEqtXr2b16tUYDAYGDBjAoUOHaNSoER9//HFRtPE/h4MXkyosmjZ1WBZEZshq1WD0aKCqxSR6Me/CWYXCIiz2+HW21n55+GHRrN9+g99/F9OGD9d8xI9R30FY3HMPjBuneey4jerr+dBDWk8FYZp/d96/kKZlKFoyP5Jo1Tc7xfnoteoqHkCqW8Ji5r8zxRcdBLVZbp1uHvA4BIrjfSwmhdcsgeOPtHqEOhXrEOwpOj5JHo6ZinIPrNRp7s+B+neyLf5fAGs1aRACTvV2A0ggVKxg0iQxYeRIq5UC0IRFWhotI4Sla/uF7drvLcIi1DfUOnLujsXiGmK93j6O+6OOeCeYqlDJ6IVJMXEw/iCxiZfBLI5xQEXHQE3VFeqHH5wMgO16gqZ7WrDk7sUOv8uNKizOJTkXk4lZ1wFomORJk4Amdlmt3MYmlWVNPxFsf/b6WUzmvEenrpm0340apaNjRx2RkSLu4eJFcR+D8H+vXFl4N6qX+bJlwhLu7Q0rV0KbeqJzn5qpdcgccjb4X6b79RjHAox5kKfFQs0I46LgnKJAhsWa5etnr3Lys1i8847InNOwobiMa9SA5mO6kI4v5ORQM0T04i5nOyqT9AwRAFzdX7se6zcSiv2YyYmwMJuFn9njj4tUQ+vWwWuvCRPZ2287byDCUtitmzhXqnVosKcoEvreheGsXi067mPGmHn+eYX5f4rBM4/wU7So6mjlfaXbK1Z3ovN+lue2E2GhKJrF4mD2fvw8/ZjQYQINKzUEv0QqVxOCdXt6U/uXh40LzHvvYY3/GjFCpMK/elWk3FfHIpYx2NEVyiIsUv2SURRFZP5C1NSpHSJSis3eK8xjD7Z4kOYRzQn0DkSXqAVrv7ZzDNlKNrfVu415Q+x9R4P8RAcs24Ur1PVU8czYHxxDfFo8VQKrcFv926zzWzewXJNrPiDlZFMqVBCpk7v0EccxOUdc0Hc3vpsP36zE3r2ifqia+wPAcFKYHhJtrJlJh/61fveKa4A5oSHe3sJ1PjDQec3DyiHaKFGo/hqDH6lsN98qLIx+YPTmfPJ5h/obRrOR2fvnwaE7OPnnCqfHBKBqMnSs34v4eBFalxxfgdfDDjK4/mAiLfE4l7KuaH7/rVrZV1h3w2Lhk5CALiFBPIicVW3PHZezZ49QrOHhohNw/Xrxu+isXKld2LaUtLBQ3aCaNxejQbbC4gasFs89B3fe702jS2sZ6vsnbd+7y5pHw5aEJA/HiWWYQgmLnJwcfv31VwYNGkSNGjX4+eefmTBhApcuXWLevHmsXr2a77//njfeeKOo21uumToVatb0YPHiOvYzVGHRrBmLF4tkTPHxYlSza1fhNmE0WoouqVmFilpYmM1w+DCJhNDq+d506SJGT1VL6QcfaO+x//1P6yhm4ktOtr1SUl201Y6U26xdK9bZs7/1/akoCmNXPgW7xChWq26XrItWtmSFOZvq3HpTEItFXGqc1ZQPkNbuNSp0/A0e7MrY3oOpGSZe/AdOXWV9zHr0Oj0vd30ZgGBPMVLvzGLhzGI78fkcDm+JgqwA2lcTfq2ZmcIT5YxNYpyrVBIuUP/+K95ar9kXV7OKjPR0OkWJfIdbz2+1zrZ1hWoaLjpgp66dyrc2QxKWlKJ+jsJCvfwuEUnLdOFUvCduD+fitQxUAcGOwdc2OpGICBEn2L49NG8itvHy7/WoHRydZ7sgf2GRkCnaUTHbeQC4K86fh12qa7tNFeCq3mEYdAZyzDkOKS9zk2TUXK0URcT8ZmSIe3nMGC1e5y4tRb71eKp+8epxCfYRo6Np2ZqwuGY5xOPHQ5373oF7h9D5ksE+RWQ+5CkA8inmlp0NiuWVoQbl5l7vxYui/W++Kdq7cqV4bqgZrK9cETGK2dlwKdGbX7kDcnKso9wJOfY+y4oCWekiC1RNG0+2jm1E5youyWJdtRUWJ09qygDg22+1DtiCBS47Q+oz7pZbRPtqVbzG7zkDeCJQ9FJvvx3q1VP48ks9772n442x4p6qVveaKGyZiwo+FfANFvfaSW+bVHO5yM4GI+L5ZPDK5IehPxDmH0aDSqLzXrGucG/cSgf7B4rlgWzq1NXqVrd4sTi+ISGahUuNyV1NH0dXqAxxn/wRGctjyx+zPgNvrXmrneUB4NXur1q/h3YVlhXq/46/pz9PVHuCn+/4GW8P+1H8YH9xL+WYXFgsLIH5+FwHRIIF24xwtWrZ38fTpwtPlOiqlu2kRMLFVqwZ/5X1XdW4sahh2EqEgZEeK2oz2GbJu5qTBscHUO98EJOWCeXVtWveRdQjgrVj/0jvs05LaegQo9f1deK63Hlhp90yr/39GtsS96H/ZyJmxZP+/bXxNCsmD+64VIH4eINdAVc/g3jeRijiuR+Xnci2/Su57w4Y0uU8Cw4s4KxXOn/UIc8Ov/puDVZfNg0baiLCFluLxcGDwqJ56JBYwYIF4iLz8xM+Z8WFmpwgNyXpCpWVpe2jxfctI9uASe0+586h7oQ1a8Sh27pVuLr++afQI6qH82lq83tGX7vfHD0KzS3JG2xjkcoDhRIWkZGRjB49mho1arBjxw527drF448/TqCNG0/fvn2pYJXwEhDX34ULOi5cyOXrbfHd2+XXjXvvhZkzRUfsqae09NQg+sZKZDEIi8REoWbS0pinf8g6eehQzcq3davo9Or14t6y9dhKSS6CQPLTp+HMGQ57NCN8ZF9atRJW3llLDnNs5stwfDB6vcLcLyJp2VLclBf3Pw0KnE27cWGx4rgYPWpWuRl6nZ50z1iu970Dvzr/8lq316hi6RecOSdezH1q9yEqWLgDBXuKt1GSp6OwUN/jvqTz98/ChLz6D0+Y/ycB62aTEhdOSoqoaxAfLyxTar2yBEI1R/wXXnCsb2LjCtWxmnjgHYo/RFKm8EFTR+hCfUMJ8w+zpo61dZdyRrIi1qtmtLFFHfFOJZBGiWJ9ey7t4XzCdQA89OkYvB1HV2yb/thj4mW6bRtEVVUs6wtwa+THVlgoThIYJGRfgx1Pcm7HZA4dCuX333V2g7wnT4r3ozVGATH41bGj6BAfPIjokVmuFw+j2bpNVwHjIARwWo64ryc//A/nzglPnLffFoNbS5aI8YOAAOHypKIKC3VA7DbLYG2wt3BlSM/SLFSqsKhV28T5hpMhahudMsNc15lxQp6uUPkIC9t3eW6LRXi41jfZsUNo4Bo1YOBAYQUwm4WgeucdrSYCwDc8AtnZ1uDU3DEWqamgmEUPrm6g9nId0EUsn5XQgGSDh/0OqRn21J7fL79guhzHyYqgXLmipdd0wTFLncmBiT+gAz6clk3//uJezs7WQY31djEGrZt7O10PQFiYOE7nqCAmOBEWtgaiRhUrM7ShyDjTsJIoDKqrITr7W+loXXjDz/FcPJcDBgMHAjtx/bp4JudO7ANaPpBj1Ccnzd5FJs0iLPBKZda/szApJmqH1KZGhRp2wqJxWGPrfQDQ+LbVcP8AGDac93u9T99KfZ2Kq+AAcR8ZXQiLK5Y6Gvhcp1nlZoxpax9Xp8bIgrhv1HunbrTlBZQaAVue5XKsuPfuv1/cwwsXCtd8gKtxvcCs41q6Jlr3H2oFC1Zw9fs97GsoLMK2teGcERCpvfTGfNHQYb5eD0EGcX4aK8ICYGtBTs9J55Ntn4DJgOGSiHn75BNxzoxGMOgtz7/UytypNKZzZ+tYG6C5GUboxL5+avyHTkE/82NT+N3zFMN/G0604XMGjIC9ivNBEJNJZIbr189A0GmLsGjRwvkOqzd0ejoMGybiOrt0ES7L6r1lNAqfs+XLna/jRlFjT3MntSlmi0XcqTR2LLe4ZU6eLFRBeDi88AK7dkFk/UCqc47vGZHve2vjRmFA7dFDxO0884zIA6EOtgBEcpE7Bmfz0UfisA8YICzdFX3EfiYm3wQWi48//piLFy8yffp0Wri4KENCQjhjO/xaRHz55ZfUrFkTHx8fWrduzSZ1GL0coGYEvHjRRliYzSgHDvIQ39L2pd52rpE6nVC3qlk3JweuVaytrqRI2hS37zJ3R21h8TdXUYAZAc9o8yyjmqr3lboPQUGi3+WrEz2N5BT7F4paSM1dFAXOLtrGRSL5pvLLpKTo2LtXuE48fmcDOCBsg9On62jaVKsds/XYBNj1GD9d/pt/zv3jsF47YeGR94257LgYNrqj4R12L9ARTUdQwacCVStZbpVU0bO2rTkQ7GVxhfI0OXR21c6YLxlEND5C7XZHrfNSd9xFnTqi86WOZo8cqRUaT7CkkUWnw6l91MYVqnJAZWpWqImCYn2ZWV2h/MR61BHQYwnH8jwWKZYRsYAAx3kBARDkKy7SqHjhgrP38l4uXhUH20ef6vRY2wrRBx+0mW6pL5JCoFvCompQVXToyDJlcSXdsZN2JT0F/viU/Qcm8fLLXbjrLg9GjhQd1B9/FO+nDRuEm97Jk8JdZORI8f4ym21iXGzS/OWXiQqEiDNnixHF6hV8rTEkL71k733z2GP294dNWRP0enEtAIT6ilGwrCztJKjCIs50mExzFqHpUM/bZgVuYOsK5aDLVGHhIlhTfZfrMeHpbf/q8PQUx27qVPHMAvt0zwBvvCEyRe7bJ9K163QKG+nOlcxAq7BIyLa3WFy5YmmkIZOavprIat2wEjrvFDB580dIfXtTnyosHntMJMMwGpnUG+qOgwHD4cKCGQ775swlfTg/wMiR+D75IEuXwj0vrIORt8ConjBgrHW5gZ1rOv7YQpXKomN9OSdY3SGHZazZfQ1ZBPloIqVhmHgQJFZaCcB22mNKTuOvv6DH3eHcyS9Qvz4bdornQOfOjrfetYxr7Ez9DT/PzP+zd95hUhR5A357wuYc2SXnJEvOIoKCARXEcHrm7KlnOkU99RNFxZw9PfHMCXNEBEVEQHLOOe2ywMLmMDuhvz+qe8LuzMYJG+p9nn16dqZnqrqruqp+9UtYCWP34SiPz0vcBAudc3oI6cRdsBjbcazH9xSTFXr8DOGlnNPdizSjkRAjniObPcrr54fLRPlKeCHrblrnEXIZRDCDs88WguqHH7pk6B4dtQGlsAPsFFGonnzStQMMYqMgLk7FUpkKvz/GiUKXmm7vTqHOOGHpwg+/iX5V1Vm7Kmf/sys9Mwq5/4bjtO/mXZhMMIn72N0m/FAW7F3g/OzXPb9Sbisn4/hIrGoksaYyumnGC0YjJMcLoSQtN4OM2EnOXDP6NevzcRuD2MjaouShKnDhZriu8wUe9diF99Dnhw6JTYwFCwwUb9MmJ7c1nKqKSPOjR8MbizRt4G+/iaAD8fHCHvrss0XM7OHDXTsVl1/uilDmT3TB4oorNJMNjQBqLFatgpN62Rh+bhor7/vSaZ9c+dpbTH8thUmToLBQIYe23MR/UW3V562CAqHkWLtWRM8HMde4y0NPiTQrXMon5FzyL778Poy77hJLu2+/FZ8lRoixOL+kblEtmwoNEix+//13r9GfSktLufbaa718wz/Mnj2bO++8kwcffJC1a9cyZswYzjrrLA4c8G4W0dTQw3Xm5LjZye/Zw6/lo3gXcd9OPVVIsh07CjOCO+8U5owpKeL0g+Hd9B/xWU5tmrnKSvGsDB4MGQPS+aLsHKbyDY9cuY/tRZ4LlR49xDytZ/A95xyxW9z55c4QJlYOVQULd7OX4mJRnxkzhEP3kSPV148vvACd/v132pLDi9ku5+TCQlAdRuj+E4/OWsHNN4v3r7/etasft208ebZCrxE4dE1KbRqLIksRc3eJXczJPSd72OVfPeBqADqlagvNg6OJ+ngZp7ebImzOyyEhTAz0NoPYlXKnvFTs+kdSzl3LprN7+Lkw2HNh8+efoq1BLHJ1yxanYHHKKZ7SnY6bKRTgNIdasHcBVruVIouIHJYc6SlY6GFyvaKqlCF+NzbG+/CQkSh2tNscFXValbOKbTliAohSvN/r8ePF7uONN3q6BMToG4/E1Nxx//oLvhMJ5zJixerYmznUkSIrqJ6rq48+Ervkl17qWpyD2N3s1s3TZE8f0N2dIPXcGTVl4T5UdAgqxOIxJcFTbT1tmljj9uvnyquk4y5YnH66q+1TIsVDYql0jRW6j8WyvF8AuGgzGNLr50OirwMsFvF7DodbHIg6aiwiKUcxVVfNT54sIkXfeacwWXGPmNOrl/jTadcOkuPFNeZaEsmMFTci3+bWQMCBI1oEiMh8MsJcEpnRqBDXYR8Av0X1FwsefU5asYLZfeGWzpt58bIulJvgbc0sZm53OKnjTyza5Gnf7q41iIqC7/o8wHBWCElPUViVu4zZEadDl9+Z0HUC9P0cen1DbNoJppyRhC86txWC4YkKzcamSrzlsjIRCA+AsBLizC5BUtdY5EbNJ0YpoZg4tm60MVuLtv0Xoxh7ZDZ33in+H+u29ldVlddWvEbac2lc9OUFmFOFP8iWw+Ievv22eBYPHtcW/GbXDfj3GBGwwD3E6SkdT/Got4JrzK/JjykxRvQphy+NRZl4VqMjSr1qPKKjxQb5o496mrVnZGjnHu8FlgRSU8Vz5h5UICwMXn1VO+/Ph9i3w/UDtghPB+SUFN8b985zUhW25cQz8y3fpocJZjEW97f2xWQw8dehv5x+bd9tE06KfQ4J1cigtGyP6MUdksT499CcDFYgfO9GjBALfXBpLDKNrufgsg3w2c9R/PfST7hmwDWwbwwsnsZR1bufVEW2S3Dfu1ab09wufMYM4fi+dCnc+vYA5uGmxpk61RVZYsoUoXLev1886IWFIoyRv8Pg64JFu3bYrriMq65N5N4JBExjsW8fnHG6jeM2MZa/+8xRMUheeSVPbZ3Mo4+KRzg9Xc/PEoWlrLpgccMNYlNp0KDqypxp00R0X52L+dxD1ZiY6Jp+kqKEYFEtcEMTp0GCxfvvv0+5F4mxvLycD/SnIAC88MILXHfddVx//fX07t2bl156ifbt2/OGbi7SxNEFi8LCCGfEJPu6jTyGsJ2/5RbhdDdjhujg7hl09bxqh4zaqsyLYKGq8MorYnd4wgQRSMiddeuELfd114mdwzVrPD+f8YH47bvvFj5KZrPYSc/IECbKH3wghJ475t7BvoJ9lEeLiygu8ZwQ3NeVe/aIzY3/+z/h0N2mjRjHSkvFxNapU/XFFgjLhlPOyIdBs4i96kruv2aA8zNFETHGAXofShD3xUsmZl2wqM15+/vt32OxW+iZ3JOs9CxKK12Dsu5c3SXNZYNatnM4s96I4JFHhPbmvR+7Y8hvDyoUWjxDYemCRZhSwdwDCyB5F5x7M70v+1+1ekydKtSfuhDpFCz0LJxVcTOFAji3h0gc8fnmz135HFSFWHMCUEeNhd1OmRZPPSHOu11nZrJYeCr56QzNHIrNYWP/MTFRx+JdY5GUJDaV/1tlszgmVvSdGk2hLBahvpoyBbZurdHP4lipWFwaFCuzZ//ASy+J39Q3tHv0ED68MTFCYC4sFP3poouExmDzZi0wgVvYxrpoLA4VHQKLmIyc8ew1FEU8dxs2uBb2Ou7Wore5NsFJiRT9xmGJo9IuttN1oWjJMTFTXb2O6uZxtRAR4SrzrbfEjnC7dkIQ+OaPJPbRsU6Chccqzwu33CIWJ2vWiHHsk0+qn5OcKNrmuDWOtnGaxqKKj8W+I+KiDWFFhJs97cA79RLP2sqw4UIT8OabsGgRWw+s5tIL4Y2iBdxd8hVXXWikKAIylTiGnoikIBJu/+5mD+2iLliYzcLi47xKzYdAU/HMXCxsrP/e7+/MvXwugzMH0+6GO9i9x1GjhrZHe/Fhcal3jcW774r8IgCYyj0Ei+SoZFKiUsDgoHe0sJXrd3433nrL9f1Fx11Ot+6CxUMLHuKfP/8Tm0MsVktS1wGw5WgKK1eKhc+sWfDLJk140BLevTv5XdKiRZ/STT0BxnQc41Hvl858iaz0LH65/BffFw8kx4mVvsPmXWNxvEI8ZzERNft9VUXXvOmcc473LnnlldAuZh0Ah/a56lBa7nodHS0ELUODVkOeJISJsdhYGsfU3lMBeGbJM5Rby/l+x/cAxOwVUu7gjp7CTUayeM7DCjNYdkJoPNyVArm5wszw1Vn/49R5Z/Dut/Dh12AcNgKjOYzXJrxD+Owf4denWZPrGblLp6zYNcYuYwSFCR3YGD3CGbXt0Udd56qqwr9xi8jkbR4KCxMTfEQELFni2iHzF4cOMac7dN15G7EzY/mgQz7PjYaSYv/kj7DZhC/rxo1i3D/vPDjh5ij9GZdwZ8wspuS9zSOPiPduuAF2uE2hxYWeJsO//OL2TGt0cBlBcM01ImH3Rx84WBR5BpP5zqdUmxilRSIrbV6CRb0Mt4qKROQIVVUpLi4mws3hx263M2fOHNLqOdHVlcrKSlavXs3999/v8f7EiRNZqsdxroLFYsHiNkkWabkfrFZrSPJtREZCWpqJo0cVtm2zM3QoXDW9K4vJIsxg5Z57fPtcZWYaWbvWwL5KoQ5Qs7OxVTn5228V7rhDNOmvv8KECSpr1th49VUDu3crzJ6tUFHhEgISExzkFxi4hddZ1f9aVqyPxGRSufVWG9OmiR3NLl1EnaKihEPc8bLjzt19wsX9zC9UPe5nSYkJtB2tF1908OuvivN/EDawCQkqNpvrvWhKeJyH+JfhRW65xUFysoNx973MokWPMr7LZBSHgtXhKiMmRgFMlNnExH28/Hi1Ni0qUIEwYinGCj5v7icbxKrnot4XYbPZeGLcE0z5fApvnv0mNpsNq9VKclIliYkq+fmizv/+t+v7/3yxK3AAJt5NXkkeqREudX5JsQ0wsTdVrMpiwmK4dcitXHvj6czuaadzZ5VrrzWSkgKvvWbDatUdCM3CeRuwnnWW17or4eGYAEdJCXarlTM6n0G0OZq9BXv5aftPUJaI8vp2rtiq8NFHVromCLXT1mNbfff/igoqHGJxExdj9HpeG02wyC5P4Oqsq1mZsxIsQvUQp5ZgVdPqHC1Et9UvJhZrRYXn9ywWjJdeirJtm7P32JYvp12sMNHYfXx3tfod0ybOaFMx4eEOrrjCwtNPR3L4sMKYMQ5+/dWOooj+c+mlJpKSVFavttG2LZSXG/nxRwMXXaQyufxB7uIx4svKnOXtzd/r877tz98PFYNF2TH2Oo8vPXuKfgwwYYLVefkpEdoCoCKeEyUnSI5KJj9fPFeV5lx62xMZlp2PPTkZRz3HshEjjMyda8B9KBVCTSZmdvBO2Q38zctvFheLukZSjtXhqFMbn3SS8B+C6qcnJ4hrzLPG0SVSzBsF1gIqLC5TrANHCgAIMxViVxSPax0xDNb/ADvLNdXI7bcD8PQUUN32Or7oJcr5e4+LmLY9mU6Vz7CeQ/y661cRUhVdaDMTHa3isFsx5OSgANaUFFbt/4vvt3+PgsIDox7AbrOz+KrF2Bw2wk3hNbZ17w7iGbYUZvAD53DO0f0e4/aePQZAWxEXtyPGFO3xe31S+rDowCLatfudlds8F/fuDBxajj1tA8XlfZi5ZCYzlwhB6MlxT/LMX89QkCpihm86lsb3tzpw31OMaLeAij5f8seVfzCy3Uhn+dHGaP537v9QUEiNSPWoV++k3qy6TkQ70N/3dh/iovQoRZFYKi3VMkjnlwuNRnx4eb3m5Lg4MCoqdlUsuM46y4bV6n23PCn8BIdK4MRxs7OM8goxxo3ttIr//dqfDh38E+AoXhMsjhw3cMugW/h88+d8vPFjPt74MQBtTSexeqfwFxjYo9jjmtM15/DddOWTBWKeHzLERlKSCpg5ckRl+HAFyKAk979cTScA7OPG4bBaef99BYsWQS27LNbr/SwqdL23jBGcHfMHS0dE0a2byq5d4qG59FIH06bZGTjQzB7E1rrapg22MWO836Q2bTDccgvGF17A8dRT2PXMn43FamWlMYdJlwFlnn6Uewv208sPDfb88wYeeEA8f+HhKhaLQorhOCscQxitLOWwmsHLJdeDJi+1b6/y8ss2TCaIopIyojlxvJIEt7r88IN4pq+7zsFtt9mJiBChi+++20BsLHTtKgSRi4fuxlw+DzU8HKu+0KpCQpToE8dLw2t8zoJBfcqtl2CRkJCAoigoikIP3WHADUVReNRd5PUjeXl52O120t3tbID09HRyfcQ4nDlzptf6zJs3j6go7zsogSYlZTRHj6bwzTebmDfPyuzNIzFTyZOnzGLDhnbOHCxVcTiygM4s3lLErYCak8OcH3/02Gb59NM+QHd69jzB8eMR7N0bRWqqEZut+lbMOSM38tXGUZRgJiwzkh/v78jHH/emU6ciNm507QRvq2I18272u1TYtIlfM4Vas34P9jmukK8nTpwBCKHz/fdF2QaDg7ffns+PP3bh66+7ewgVADfxX67p9B3Jj00kJsbKnDkwe6fQ+WeWZTKnyk7I1q1JwBhKHQkAVNgq+ObHbwg3uGxfd2wX9yyWYub+9huO8HCqkl2RzdzdQlDKyMtwlvNF1hcYs43MyRb/m83w+OMLsNkUXnxxMPv2xVf7LXKGMPf3ueyNdpnMrF4q6olJDBCTEicxsmwkW1esIytrHQAvvBBDTIyVFSss2v2LAM7gBEk4UJizfr0wTNfrnB1NZKSNPju2MhwoyM7mT63eg6IH8WfBn0yfPx0OnIxamsrnn8PEifOxxwqHvu152/nhpx+cSbjcMZaXY3GIHbWi/Nxq9x0AaxrQlgPFsYzMjibBlECBlsMi1VbMvN9/x+YeFrcGsnO6AX0pIYbffvkFi9v2b9s//2RIFT3yvm++QTlLtOOCdQvocdxzHMrNF/cw0iB2QP/8cz5XXpnBF1/04Pzz1/Lzz0IYjoyEF16IJyHBwvr1FaxfD2PHJvDjj2PZvFlhM7eTwEGGL1zIkbZiItiUs8njfthVO8W2YmJNsbyy8xWoEOaMO3et5pDb81ATqgp33NGe7t3z+eUX165t8fF94oUlju9/+Z5UczqFhZpnd0QBD2xsi0I+W0+cYHc9dwn//nczJSVZbN6cQvfu+axY4dr+tRLGTPs0Yn/6qZpT+ObNycDJRFLOn0uWUHywbtfoC4ejP9CJo5VxrPpjFQYMOHDw5dwvSTILk4tlq0uBgUQYi9i4dSv73a61bZxYxJXmZrGnbxYJhcd4bFA+H/YX49OMrjN4+cDL5FnzSDWn0ss4kgP5G7lqF7wxFGbMmUFZJ/Ebu3bFA6diNFYw76tvmaRpAd9e+TP3HxBzyMiEkexevpvdVFEF10BRSTGY+oEtkvP4gW37s9jmdg27dvUFNPNWg5XCIwUefay9VWgNik9+hqXbfualoc/z+cpRjAlfyhLLcOKiKrjg2Qf4IO9NxnxkJd4UT6FNaHL+lv43+uT3oVtYN1alijwss/ePhP0QEWHj8ceXYPx1C4/3vY0Ks4N1y9eRv8HTFC1Z05rOOVh7H5s/f3619woPFAD9wBHGl9/NJibMU+t0olTsrEdS6H2sqYFMdSAH6UC4YsFun8+cOd41nomK6A95x4zMmTMHu2qnskJshIxOXMimTdnOXE2NJUwLNHD7F2O5MnIzN51yE28degtVpFgk/Nt32VORRhd20zZxMXPmuCw/lKJwoDPPMg20Zigr+51Nm2zAWeTluZ5Hh5tguDgigvyf5vDUU+MAIVjklpm93s8tS02A0FItZwRoin5dqIiLs3D22b9pkefPJp8kyolgz8SJ7PLSvjoJmZmMBSxbtzLPT1qLiGPHmNPX+2eLNi1lz5zefLbrd+asKeDJc4fRLtKLuXAVNmxI4eOPe1NYGMall25j1qwsdMHeYlHo3vUEv+4eSAcOct1t2/hlsYM27Sq0KH8ZXHLJZubNE9YisQynjGgW/LaCbcddbbN58yCgPVbrVvbvF34n27cLB25wKXUyli5lGFDYrh1/zJvntb7WUqHhzD6hOp8vb89ZMCirj/mZWg8WLlyo/v7776qiKOrXX3+tLly40Pm3dOlSNTs7uz4/Vy+ys7NVQF26dKnH+48//rjas2dPr9+pqKhQCwsLnX8HDx5UATUvL0+trKwMyd+VV1pVUNXBg23q0KF2FVT1Dl5UrXPm1Pi9xx+3qaCql19aqapiPaJWVrmOq68Wv/fYYzb1u++s+mnOP7PZoT75pE29916bmn/Rdc4PbNOm1anuW49sVc2PmVWmow56c5BKj+9UUNU3/7ne47yoKEe1sm+4waZWVlaqmzdXOt+bNs2mLl9eqd4+8A/1BAmq7YYbnL9xKP+QanjUoDIddeexndXqsmaN+J0Ujqqm6UaV6ah78vZ4nDN+TIWoHzeqlWVlXq/p2m+vVZmOOunjST6vu7S0VP3222/V0tJStbKyUt21q1Lt1k1c49dfW9Unb9kvrqnbT+qP2370+O4n7xSJzzouVNs+l6keyj9U630uLnbdo7xTJnt8tmFDpRoW5lA7d3ao5d/PUVVQHVlZzs9/3/27ynTE39+mOH/nhRdsanlFuRrxeITKdNStR7Z6L//oUTUsMlsFVZ3+0Vdez3n21p0qqOolMT+olZWVal5xnjr98UMqqOoFfKFW5ufX+Xl49VXRr6fypVq5d6/HZ/bx49WqHck+dqz69qq3Vaajjnt3XLXfS7jmPBVUtWv0do82q+vfRRfZncXdwYuqdcECNacgx3lPC0oLnOde/tXlKtNRe7zSQ+WhMOf3ji5Y0+hxYuFtn4nfS9qhrjy4Uj161NUnxs0603lvrO++2+iy3nlHjBUXny+el3DK1YrC4mrn/fCDOG8Aa9TKDRsaXe5VF4ln43HDg2plZaWa+VymynTURbsWOc+54F+/qqCqbdt+qVrfftvj+xUVFlWJPqaCqp4643512FvDnO10yw+3qJWVleqOozvUrzd/rRaXieux/vST+m1Pcc7Qt4Y6f+vXX8W19ejhUCs3blSPRKPOOD1cTX462TneHSs61qDrPGvGTGfbvRFzsVppsbj60OWu/sbfz1L/78vbPL67NnutynTU8IcVtSgM1frRR+qBfRWqNTxKXcoI9fPZ37ued+0v87lM9aN1H6kWrZwn/3hS5e4MVVFcfejOO8V4bHvlFTX8IfG9Xcd2Nej6qo6P7n8Fu7KdZW7et7/68xovPj/nmsvrXe6QjkdUUNWzxhTWeN6l7WapoKqxY15TKysr1dzCXJV2S1RQ1U/Pfbve5db0d2P335zXqygO9cSJSvWnbT+pbZ5to3Z/WIxNCnZ1NQNV68KFHt99/YUSj+Hu1lttqsVSqVZUVKpGo+ec2p79Yuw3m9XK8nJ17lzP+b7ryf/ntX5fvHKg2tx85ZV29eKL7Wp4uEP95BOrWllZqVoslWpEuOibe+hUba1R7W/DBlGfhAS/3UvrokVq50t7qyTuVK+4d4366fpP1fQHw1Wmo7759N/Uvcf3qkoXcb8n3flDrb936FClmphYfW1iMrne2/CL2311e07d/4rLitUKS4XaTRHz4O+fec7p55wj7tvrr9tqrI/t3/8Wc9rVV/s857O/famCqo5J31bjcxaMv7y8PBVQCwsLa12v10tjMVYz4ty7dy8dOnTw6mwVKFJSUjAajdW0E0ePHq2mxdAJDw8n3MsutdlsxlyHhGmBoHdvsauyerVrt/hm3sQ0cKFPP4BKeyWbLHOBSaxZb0JFGBaZHQ6P7+h5ZNLTRRbWr792OYK/8w6Ulip07WoUz1AHzTb28ccxPvAAxjoYmM7eMhurw8ppnU/jrhF3cc7rYlultNx1Px0Ol1/Vww8LR+v0dAgLM6IoRvr0EZFTd+6E6dONREYaGZb4KFAAw4dj1H7nvQ3v4VAdDM0cSreUbtXqolvcFZBAohLNMbWIYluxsx6ffAILtIBhQ1iFOSKi2g5spb2STzYJM6gHTn6g1j6h95uuXUW0h507YeBAE9/u1ZyOKxIptR30+B2bRQs3Y6pg0eW/0Tah9l0VsxmiI2yUVpjIf/Ydkt1+74UXhPP93r2wcEcHzgSUsjJnmad2OZVJ3Sfx086fnBlgAb75xshddxnpntSdjUc3srdwL73SelENRcGm5WNokxLn9Z60zxT3MceaitlsJtmcTIRNM/WiBHNkZJ0SEoLLH6GEGMwGg+t727aJjONVMKxbR59UYfe7M3+nR/1UVaVIizITbxT1qe+zrodnf+st4eNicjjIiM8gOTKZ4+XH2VO4h4EZAwGcycR2nNiBwdIG3dI2KSXc2Y8bSlKs9muWOCocRyjRo4KYyuiV0RnDMRGH2pSZWed77YtrrhHuK3FhKt/EVGIhgtwDxXQ8yfN3dU14JOWYI1IbXW5amng2TjgSMZtMtI1rS05JDkcrjjrb7HiBGC9jKcIUHl6tzNSeuzi6JoWFf1pg1Apiw2L58PwPmdxrMgDdU7vTPbU727YJH7FpZ7WnjaYYOlJ6xFmOHggr9sAWzHv3cvcZ8HGWBcotnJR2EvOumOeMrlZffnxwGum/fE3e4qn8FNWfm8vKnI4uut/M0CE3sLLHzyRGPuzRX/tn9KdrYld25+/ml25wocVCe1MeBUoZ9167jCVbhRbrsn6XcePgG1mds5rrBl1HXLgrIcPojqMh7t8kn34DefPfIzwc7rnHiNlsxILNmek5KTqpUfOit2ctLsGloSgqtVT7XDdJSo+y17vsvqemsep9uOiauBq7YluT0FKWF4vxrKioyDk2pkfXv9yaOLfLFj7ZOZQSYlFVhcmTzfz3v2eT868cbv6Hg53AZNNPDLKtFZEb3Mpu09H1+ptvYMoUI/puekqKZ1b7HEM77CkZGK+9itKKCGfkIUVxoKoGiqwxXq+r0iJ+r49hG2UdMigsjOOJJwy0ayee75W5Kxj7wT30Te2LI/b/wNKenA8X0Lm2XDmaplkpKfHb/Tx4cBN7F78F+d348FmYNWMgbyzfypHY3RwafJgZv7yBuudpAH59dzTmF2su95FHxPPWtq24n+vXC9/V2bMVPv9c+G32ixdafSUjA3OVRCW7T+zm4i8vZl3uOkwGEwmxy6EIyksNHtesR8NLThbPmE808xTDwIEYfNyz5Dgx/p2oiHaWEar1a33KrLO70oYNG3BoSQ0KCwvZuHEjGzZs8PoXCMLCwhg8eHA1NdD8+fMZNWpUQMoMBFdc4WDcuAP06ydUoxfwJb2SjnmGUtL46+BfTPlsCoPfGsxnh0Rssi1bFK5UtPizVZws8zRfMN359/zzhTPqnDnCAUyP7MSePSLagtkMd91VZ6+1pYeEL8vU3lPpmdITwrWoUKWuh8fdp/+++4TTUni455p+5kzh3BQZiRBy9CD+A8WCrchSxH9W/QeA24ff7rUuugOqDTOJNnHBx93ilL/wgjg+yOMMNm/0Gut/Xe46KmwVJEcmOyMq1ZWYGGd1SYzXFoEVCc4cEjolpZp63lROXFTd4/CmpIvZ/p+PJHHhhUIYS031jLh31j19uY63sZd6hgd95axXmNBlAlf0/KfzvSVL4Phx6KqFK96d792cQ7XacGj5GNKTvMSbBTIzRN89rN13gJJi8V5dcoa4o4e0LSbWMyrUAw+I4+TJYgZYsUI4ChYW0qNUbBYcKjrk4WhfXFmMQ4uilGDwHjK1Nkwm4ScOWoJCLQ6pHvpTj6hlsVmcWXWfnfAsCy8VfTiWIowRjR/046JdPhZFliJXNKvIfDJiMlyrDB+bKvUlMRGMkWF01cx8dmz1neyxLs7bdSE5RTyTeaSA1SquC8gpdgWmyC8QfSKBQq9BAYaOFJVSFj9I5+/2cY95N+f2mMxzz4nx78EHRb+fOlWMO7e+2M0pWOSW5DoduHXn7ZiKY1hmPML3mu/rxX0v5verfm+wUAFgUAwMGiTG2LUM8nDg1sdse7QYu9wFAhDmxVN6TQHgu55aRbdt4/mRsERzCE2MSOT5ic9zSsdTuGvkXdV+Q49qVjjifRb0u40//nAFmSu2ucwbYsNj8TeK0YBiEmXk5Xs6aBcVucIpZ7hlVa8rzzwjorhdfXXN57UziXnKVpKM1W4V+X0qxFicGO0lznAjOKfbNgqJ5zVEPo4//xSRgZ54QuHDD8Qzc4fteXFyFX/UIUPEEJeV5cpno+MuVADYHQYOr8pm/SUz6dFDRIRVFJg4XHgVl1R6z/Sn9/Nupr08//wf7Ntno50WVXj1kWWc+dGZ/HXoL95e+zaVUfsAyAn3HU7ZiW76arN5j93cAN5Yuh4OuublZ5+FRb8+Ct98xK/Hy3j/8wLnZ5bCRHbuVH3+Vl6eCFcMwtd82TLRdzZsEEGtXnxRRN90BsWpGh0AePSPR1lzeA0O1UGlvZKjCUJgLfHs1s7ofVWDeHige8uD8ND3QZIWOS+/MjSm+w2lzoLFgAEDyNNGwQEDBjBw4EAGDBhQ7W+gvtoKAHfffTdvv/0277zzDlu3buWuu+7iwIED3KzHIW0GpKXBHXesZfVqG6Wf/cDnXCxW/FUWvuty13HGR2fw3fbv2HR0E7RbBkNfx2BQ+Ui9jJ10qyZY6PNVSgo1s3ChOA4f7oosVAt2h51lh5YBIqxptDna6bxdVOrqRu4PmXvoP58cOCC2EUwm6NuXY6XHGPLWEHKKc2gT04aL+lzk9WtRUWBSxGQUVyHCZjgjIeGK+T2Fb30udJcfEvkehrcb3ijtmzOyT0UCBRUFHp8VFGsLXHM5cZEJ1BV9bJ47V4RDffpp1yLEPTvsO1zHX8UneXy3S2IX5l0xjy6Rg5zvORwi+2fXRE2wOOFdsKgosaEPC74EC33MzbGnO6ML6oJFDKX1Cq+i57fwiAq1cqUY9Q0GEZw+KwuGDnXG6EvOLSIpUtjg7zrhip2+cN9C0Hw94o0NEyzA9fwcI9W5Ta+H/tyaJ5IL7snfg4pKbFgs/xr5LyLtog/GU9jonXyA+BjtXtgjyM7PcwkWEQVkRrdxdQZ/BsswGOihiPu5c0f1Sdr/goXoJ8dJBquVDnFaIsLCfc5z9Oh5yQ7vgsUbD47AaHKgliWzd21HHrk3lfvug3vvFV3oySdFe27VckJ+8k0kaokQxirtlc7nVR+3jseV8lvBWorDIcMWyacXfCoiMzWSs8eI/nG4eBDqMVc0oOPakGWNFoN3nJfF/eSeQvvyYw84XnKUZ+Y8yIuav/o/h/2TNTetIT3Gt4CZGZuJEQNWI/SI/9NjHVPsEI0a5TBiMgQgCZfRiKJpD08UeUaSFHkKDZC8nY5xteewqUpamth3qG3o7qg5VFOWSkFFgZgnNI1FYmz9BZoa2bkTAyo38V9eSnuSU08V2rCHHxbPzynDKhjLH0KCqJLmu0MHsee3ZInvITQ52RWue+06hQkTxLzfo4eYJ0b0EnNyRWWc1wSiZWXivSilAqNRdc7Raw6v4cyPzqS4sphIUyTjO4+HWLHIPnCoDvfI3aeu6kq7nqgqPPhoITP/8xruS9SHXYnfWbb6bOzLPNd9b71f5PM333tPzKmDB4scHRERou9Uy+GsJ9p0iwNutVt5e83bfLhBSCY/XPoDNw++2bmxWjVfjz5m1ShYbN8uHv6ICNcOpRf0TcsTbolSmwN1XgHs3buX1NRU5+s9e/awd+/ean979KwuAeBvf/sbL730Eo899hgDBgxg0aJFzJkzh47ugfGbEVE5uzCgeqYX1Xh6ydMUVxYTZtTUcUYbTLqNUWPFIPkVF1TXWBwTnTDVUj30qgeLFomje3zCWth8bDNFliJiwmLol9aP6LBop/N2QalrZNd3RKKj67i+1LUVfftCeDjPLn2WnSd20i6uHd9d8h3hJu+JiBTFFdovpkIsrvRM06rqmrBTyPO50FuWLQSlEW1H1KGivkmI1wbwioRq4WYLS0QbGQ0VhPm4Fm+c5CYrPPSQWFfPmCHGo7w8PCL6LCjzXv9Cz6owdy50SxJmZbvyvSczKilwTSJtEr3vemW0FQ1bSoxzUC3WxvRYg/f46b7QNRYegsXXX4vjhRdCnz6uk3UhuLycHsnCaXvniZ3Oj/+z8j/O6FSxpoYnUNIFC3eNRdUcILtO7AIVusT0RVEU12TiY2e9vsRGu0IYfrfhd+cuGBH5ZBAjJEX3yvqJ7qa9AOzYVX215m/BIiVd/IYuWHgTeouLRV9LtRV5va/tMyK58QbPgea558Tx8svFsFKVWw0fEq+ZlulaJ33c2tS+REShAaY4elSLYtRQ/j7hJFBsOMrTWLHblctCH6csMbrGovpqZFT7UaQ4IiiIhJTKx7kvbjmlYTAqti8vn/myMxyyL4wGI+3Cxdy93+C5CirSNBaxjgCZVhgMGDWNxYKdy+nychdeWf4K4IoWRs/vSDZ638TwBxkR2jWXpvLRho/YnLMbbGJFnRDjZ8FCS2Rqws4daZ/y228ibwGI+fDl23eJCHfp6V4lorZtvScm/egjkQfm999doUuffVYIFT17wvLlQkPXLlGsF9SKxGqbXABlZaLMSLeNl/zyfKbOnkqhpZAxHcZw7N5jzL9iPsY4oSbZsa8OgkJYmGueLa3fHFCVy245wJPT40E1Edv2V378qqT6WmLRw5A7kOhYG+lnzwLg+59EW9rtIrx9VpbIcK1r/AFq3YPWNBZqRhscqoPjZcc57YPTuOGHGwAY12kc5/Q4h+mnTnetf4o8w83qc0E1ocUdPZLpsGGu0OZe0DUWFY7wQOYE9Dt1HjU7duzo3NXt2LFjjX+B5JZbbmHfvn1YLBZWr17NKaecUvuXmir79oljZ09Vo6qq/LHvDwC+uOgLnhz/pFO93XesWNh8yYUeKkebDfILRHOmTPKtWgOctn3OZBB1QM9sPaLdCIwGI5GmSFe4WS8aizoGBfIwg8ovz+eNVSInyZuT3mRY22E1flUXLCLLhZmCbgpVVuaymU7meK0aCz1XRUNxDiC2SI4XeQ6qhaVabHKlfqPCQw/BtdeKLjJjhrAEeughsTNlNguzjjeeE2X97jjFa6g6fTGqR6OYOxc6x9essdh7SDekzyMpyvuWS3SCmXjEj+uaY73dY4z1u06vplA//yyO557rebIXwWLTURHOZX/Bfn7Z/QtUCsEixtx4jYWHKVQVjcWuE7vg51fYfO/v/PEHFOQL4dJfGguj2SCSDQLzNy/n0BFt1zUyn8w8bUMhtfF+DlXpHrYfgJ27qwsOftdYaOZ+uimUNzO9Mk0ASLf5Ftgee0z4crnvz1x7rTB9WLLEFSL+scfEBuEc6wRK39wNO85yChZFxdriQEsUF2aD6xPGN/oadVLjY4hIFpGZvl4l2s9udz2j5XGaYBFR/ZkzGoxMjneN6T3y4I2lScz9x5I6a1o7Rgo1436z5/hU5BD9Kk4NUJx8gwGTNia8v+4r9hbsFXmQ9qn8pOco7Pk9ySbvmxj+IDVSG5zKUrl73t1M++EJABTsxEbVX1NSI5df7hq/TpzAYBDa5u++E/kNBiSI56u+msbLLhNat379XDmt/tR8CG++2TUHpSVpz2VFIsfKjlX7HefGn2YquuXYFsZ/MJ79hfvpktiFHy79geiwaAyKgYRUcc7eg3UcS/VJvxEai9fftPDpm0JySh99JyvPm8WkqTFceWX1cyOSj7J0sYlTpwrzr50bEjlxQph+z5ghclPoGn+7XZiXXXZZLRU4fBi7AuenL8T4mJGUZ1P488CfxIbFckXWFbwxSaxPEiISnBqLE0Uu4VRV66ixWCLWU9Rixh8bp2BAM4fKr/HUJkWDE+T95BwVYNq0aSQkJDBq1Cj279/vt8q1ePSsXVU0Fnvy93C45DBmg5kJXSbwwJgHuKD3BQD8t1RkzVvNEPKOuAZF3XFbwUGi9YjvlPc2m8suoF+/OlVTVVVmrRG7AuM7icnWbDRj1CT2wjJXN3LaKtd1A2qViIXOwIH8Z+V/KKks4aS0kzi7+9m1fjUxXEyK4WVCsNA1FvouoNnk8JkcL6c4h935u1FQGNp2aB0r653YWHHfAfJOeO6AFWoJ28LrafN/yinwv/95ZqmuyviJYqG1lFGUn6je3vqiZdIkMeYfOQKVOWKBvCd/Dw7VUe07G3dpwmrSbt8212FhZCBUxodzxIK6sYKFU2ORnS18KhQFzjjD82Rdb19ezpgOIqb/F1u+QFVVYQYFZNiFwXCsHwSLUmKcCQ51jcXO4ztxqA4hWKz4JzZLBKeeCrt3ivP8JVhgMJCoihmq8mAW06drWrH4/WTM03a7Jkzw8eWG0yNChJpeuzW8mqxaVqqZUVDml2xiuo+FrrHolii0abvzdzv7pqVUOP9mVnrXWIBor1mzxAJORzebiI8XpsybNon39MzqtpL2sPQep2BxvEDrt2El3LEMNr4Bg/qe3uhrdKdDG9Fui9aJDpafj8uUMF6MXXGR3lcjT173CTPWJ/Ptp6JuN1/6PLE+zvVGxxjhVLE/3PO5KHaI/+MCpbEwGjEZtHtr1TYG8jsxeKiNggIwJOyB9n8FVrCI1oRyazRURjn9K8JNhRjC/XzdigK9xRjL8ePOBj7vPDj9dFyZ1xthwuiebE1P7qnj1J6XJ3KstLpgUVYunjlrRBmqqnLJ15ewLncdCREJfH7h58S7CbZtMsQzmJ3t4JVXYNr9No4VlpCdLRSmqioW7zk58PjjcG7F53zAFY3SWPzf42IiiR/5f+xZ8TI9rxHZc59+Wmggrhq2iYhLT4PJ1/DzX3vJ6mvnpAwjpG1EdRhIThbJOUFkLf/Xv+DOO0U9v/uuDubZOTk8fTJ8p251vtUhvgNLr1vKB+d/IHxLgXBTOAazWP8cd0s6WFLiUib7FCzKy13puE8+ucbqGCLCSNRiDx+r3pxNlgbNDk8++SSRWgv99ddfvPbaazzzzDOkpKRw1113+bWCLRofGotF+4Wp0rC2w4g0i/vcL00TAqLzIFHs6G3c6ppodZPrRPIxYRfeXN7YtUuYUEVHVyvXF7/s/oW1uWuJNkdz4+Abne+Ha7tfheWu3UunxuLQNpdzki/sdlgsotuUjRjMy8tfBuD+0ffXaScuIUJMiqW5PcAS7fSxcJpBJdiE2tnLguSnHUIwHt5uuNh9aAQGs5FIo1gEHjvhuRorKRODTiT+12N27xtGWw5RSThL/6iusdB3TtLS4LTTxOv1izMwGUxY7Bayi7KrfWfzHiEYhcft8W0GEhZGJkJVkXNQXF+x1u6xpnrEusblY1FBJLYKmzCKB2H7lepKNFheDiXmROc/F/W5iAhTBFuObWH14dUsOSh2gFKsYmc2xuQ9e3RdiItz+e8czxf3oH18e+d9yynOYdthzxwO9/1bi0blL8HCaCQOzb5szusUHIuG1M0YTnma1M+1TR1fGdkbwYiYTaRxhMN5YXz6qedn5Zp9tt80Fpo/dAEJ2MqtdIzviAED5bZycopzKK0sxVEudkHbW2q/r+efL3Lkvfee515NRITLJOqMM+DXUx/XCu7kFCyOFYixJEIp5aW50OPsK0TKcD8yOFVsomzZ2h1wjVNxcSrFUZq/mBdTKIC0+EweuuMrJpe2I+y5F2v3WK5Cx1ixzX0gwvO5KLJrplAETmNhNmpjQkUiJls8zP6KE3lmuvUqw3H1qWBwkBaWEJjygZgIG0ZtY2fW6N/5boqYXzNs+X4xW6xGkvD/wmKpvsHnB8FCczUDhFJXd8QHXFngKxI5UlrF4xso0IKJ/PekI6wrXse249uIMkex+ZbNDM4c7HFuh3bi3mxZkckdd8CzT5tIS4ihXTtITqskNVUs9tu2FUL7jxUTuI+nGyxYbN5WyYnsZDBU8nzBS0T1znJaVaSlif2m965bys9/LeC7+K2c2n04/OMfdHzgGej1rfN3DmmW4A8/LMwiX3zR07S4JsqOZvO4ZgRzaqdT+ceQf7D8+uWclFb9B8JMYtLLL3Zt0OlzrslUg/vq//4n+kHHji5zAl+EhdEHoel87rnGj7nBokGCxcGDB+nWTewuffvtt1x44YXceOONzJw5kz91/ZykZlTVJVhU0Vj8uvdXAOeuLOAMcQlA2kYANu5w2ezr0mwq2ovvvvNe7kbxXfr2rdOuY2FFIbfOEREubhx8o0d0lAhNsCgqc034TlVrZX7tO6rr1onQIPHxfMB6jpUdo1NCJ/52Ut0WTLpgMX/BdfD1R06NhS5kJcdpi20vC5Ifd4odg3O6n1OnsmrEYCBeEbsKu3LyPD4qKRODTgQNX+j6QjEojDOJ5+33P6q3pXt0Cn2NNO8Xo9Mm21tkqO37xOAVG7vXd8HugsUhcX0lmjlcfRf07pqt0g+/hv+IaGDcfz92u3AH2r5dONx1/m0WJ0iE8nLiI+Kd0XJum3ObM/RrvFXsBjdGY6EokKKpuY+dEPfDZDDRMV6oj3af2M12N+fmnj1d321Ltt8Ei3i0WUoz9eOCS8lMrcSQnSMatapGxw9ERsLdiJBqTz3l2lEH/wsW+vpLxUB+nh2z0Ux6mHBC3nViF0dLj4JFLLTTffhYuGM2w8svw1VX1Vxury6aVq6oPTmFYqF3olD0l1hK4O23hXTiB62MO6cnbQXFTtGx9hw65BIskhId2LTbWWOAh7Fj4eBBsQVbTzokdQJgf5RnxJ4iVdNYqHX3/6oXBgMlWvZgvpyN7fECyB0EUcfYdVZPSDjIxZsgNazuEfPqixJmJsEhFoE3TBrG3TeLDYtECgIjWMTEuMYAvZF1/BDN7bLL4J574NVXRXhsdxKTtT5bnshfB5dV++4JTYNOWCmP7hGJH6f2nkpmbGa1c3t1890nCo6HeVyaHlkqlwwKjzRs7H3u/c2iau2XctX2YiE8V91gjIri1H1wXq4mgM+aRcdC4OSZpFx6n8epp55a/zr8atxPuVmYDi64cgH/mfQf2sS08XpuhCZYFLpZfrnPuV73RgsKXJtn06bVPleYzTzPvzBgZ/ZsAytWeK9LU6NBI2dMTAzHtV41b948Tj9dqIwjIiIob04eJqHkxAlXOAE3e5e9+Xv5fPPnAM5Y7ABjO47l8XGPc9eIuyBdCAfrdrn0es5Qs2gvfv9dmBkdOiTsANasEe/rgkUdRfh759/Lnvw9dIzvyMOnPOzxWZRZPFGlFdUFixhKat+5+EP4kXDyyby19m0Abh92e52jkyS4775tn8Kx4gLANZYnx3oXLBbsXcD83SJs8Tk9/CBYGI2k2EXZ2UdLPUKglpaLhXe00vCFbk2MjxSTx4Il1Xcc3Z3IdMFi6VJoFyYcog8WVs+cvD9b9KnUqJoFC6cpVLYmWGhOgTGm+l1nWBiYEO1U/KPoD0dvepjTXjuf1FSxlurVS7ji5Fni+J1xzl3AB05+gISIBJZnL2dPvggaEVEpFijRYY0T5FI1h8+8Aldf1H0Adp7YyeG9CQAMG2VhzRr45L9FPM/dTOMZvwkWbXHTKLVfDG02klGpTfZjx4o4zv4mPJx/8AbRETa2bvVUOvpbsDCbIV4RnbTTuE4cPQoZ4ULjtPP4ThF21iJMZHyFm20IGZ3CMSqV4DCzR7MfLygUfTDOUSq8Pf0sVAAMNpggU2gtPv7YNU4lJLq0jTH1MG+qDx3ThE/S/jjVI+iH0xQqUBoLRaFduacW0xhRCpeeB/GH6FkezZs/EpgFvo7ZTDSuMXm3tp+SSL7ffZQAsaLUpWbdRlnHDxqLqCjhuH3bbdV9GROTtNWsamLuluqbvMX68szsapOr+1/ttZwBvePhvGshZSuGzn/w+Ntruf/RPK76+F8oo56HqGMw+Wq4uy3nvH4b6WFi7bFjl+9nJz9fyAt6YEoQbmyrVsGXH4u+f3LEL5gceJcMdFumsjKnaqJjARBWTmHvF/lstpiPLr64zgEvXZSV8X2G0BJP7japVquJKKO2seoWvKZW/4p//UtEnureXTiC1UZYGENZxT1pH9C1q0pMjH/DIweKBo2eEyZM4Prrr+f6669nx44dTJo0CYDNmzfTyUuEI0l1FF1b0aaN82GxOWzcM/8ebA4bE7pM8HAqVhSFB095kGcnPIspRTisrtnjSj5UTbDYvVuYk4wYIcLKDh4sHIVmzBCf18G/Ir883xli7f0p75MY6bmrFG0Wo1RJRThffSVixusSu/tA7hMtOtXaMd1Ym7uWMGMYV/S/ovbvaSRGeS4ec/eKwdxpChWnfe42eXy84WNO++A0ym3l9EvrR1Z6Vp3L84nBQIpaIF5XxLP52GbnR+VaFaIJkGARvxqAv9ZHc889nn5zBflikE14/B46dxbyq90OxiPCp8Sbc9/hw2JEzIw84LtQk8mlsdCct4s1jUV9NQWKAjEGMcmV7Bd996kj17BggXdntaWMcmZgzErPYvn1y52O3D2Te1JpEc9EbFjjBuCUCNF/84pcC64uCcIGYfGBxTiOCnOW/n3NREXBpeeWcjcvkqbk+WXRjdHIq/yTSYlLMJgrYexjAGQWaSqEoY3zC/JJRARxFHPBSCE4uudN0fuWvwQLgJPNKwAoKzfw88+KU7DYmreVJQeWQoXoj3HUrrGoK4Y2aSSbRf8+sF/020LNTjrBURKwRW53NRGGCOfP++93y1UQJQasWAsYjIEpu0uG2EzYnQiV+S6tapEqxvBYAqSxUBT6ufl/jRsHq5dFseGJt1h5w0rWrxtJYgWBFSxMJu7hOfq1OcYjj7jePkpa4MrV7fyqaiz8IFjURFScCTNi7Nt04JDT1E+nRPOx0AWLe0beI0LLeqFjfEcY9C7c1ocnPviLB68byMz/S+G9vz/P1u/O4Zl573PdtSaUuMO8ufZ1jmSIdcmOfb6FtZdegvffF/3gjDOE/1+bNmI4KznYBcKKmZ7zqZCYvK1R3AJ46LsebYvBqCpYHVbGnJXLpk3C2qiuHCg8wCVfXsId/7uQb7Scsef1r91qIlqft9x8TGuMCFVc7BpQ33lH2GhCtbDAFpuFWatn8dKylzhn+yOceTn8X/LLrF5to0+fKoJqE6VBgsXrr7/OyJEjOXbsGF999RXJ2kO0evVqLr30Ur9WsMWiO1D36OF86x8//oOvt36NQTEwY9wMr18zGoy0S9wBwPaDiU5HoWqChU52tisVtr79mJICZ9fsHF1hq2Da/GlU2Crol9aPUzpWj74VrQ1OxaXRXHih0PC9+KL4LIZaIkOoqoiRB7yXKlanU3pNqVfM+OgIz4gex3YJzY9TYxHjKVhsObaF676/DoArsq5g/hXz/ZM93mgkQYuSREUCG49sdH5UXqHt5BOYnYaO0Xl00ZKaPf+8iBwFVaJTzPkE7Hb69xf/2w6LRcbR0qMev1VZCfnHhXDWOaK6/4UTRSHTKL6bo4X91gfXGHP9NQWx2gB9mAym8wgvftvZeT1btrj8MACWMNrDbrlHcg+WXbeM+0bfxxuT3qBY057FhjdOY5ESpQsWrklS11gs2LsA8sQM1Lu3NoRafZvdNQijkQxy+bHHv1i1bxujxpWSFJnEheu1cgIlWGhakKvHiD41e7ZQPO7dC9/+KISJnmz3m2DxXec7ORcRd3T9eoWe0cKubO6uufy6YwmoYuHnrzC+AERH09awD4D9+8TzWao5pifZAydYRIXH0KHjR04fOZ11hWJnOdaC3+5rVbqkdCepHCrMsP7ASuf7xap4TgKmsQDyHS6znx9+gP79Ffql92NI5hDCrdoEFmCNxT95jQ23v8306a41fxYbAleurrEIsmChhLucfSlP5Nc9v3p8XqrNR3FKBa/3ep0nxz3pcw7s36Y/SZFJDGgzQFhKuNEzpSf3jr6Ht897mw/O/0C8mSzWJdsO+PaQdreUnzdPRHDKz4fwqAro/iOjbruEMcX7xfjmrW3cNRbaesbkgLYW0X/3F+ynb9+6B4/ZX7CfQf8dxOzNs3nlxM+ciIIejiRO6VR7KP4YTWNRUuZ6ZmtMjrd6tfDs7tDB6bT9+orXSXk2hVt/upXx74+n7QttSXsujRt/vJG7frmLn04s45ducDAyv/4amBDSoKcqISGB1157rdr7jz76aKMr1FpQdJMkbbV3uPgw76x7BxAhZoe38x0ytmd0LvtM5VRURrJ+vRDudZeKNI56/1LnzsKz6667hKo/IsL7eQgJeux7Y1mRLXYTbxh0g9fBJz68CAxWcIsooquZa9VY5ORAbi4Oo4EvTojR5vJ+l9f8nSrsK0ry+L9kb08cqoO8PLHYS47Wk9OJ+r207CUsdgsTu07kvSnv+S1GPQaDm2CRyMajLsGiwqLt5AfAxwKAiAhe5C5uSfmc7LwIXn5Z9IVXXxXZWQFRtz17yMrqzvffQ+mhztCGalFD9u8HVTWAqYwuppp3RrqGHYRy2LDVzC+/QHGZCQN2p29CfYgxloENJjAfuzYkDR0quqqiiOzxe/YIzfgaBlFe/DnuU1diZCJPnS4y0xdXiEk1JsyKF4VHnUmJEsLOowvG8PfjYjHSJVFoLA4WHXQKFr20Ha5ACBYA2O0MzMxiybVLxKx1n6Y1HDzY51cbhSZYjO16iC5dxH2//XYRHbG8XOFUfuccfvSbqZCxXx8u2v4FP3Ae69cr3HTqIEwGE1vztrK1WLSggkOMJ/5aBIaHc1LlPtYCx7KjyC3JpVTTuKVY/ViOl3J7n7BzYOrljMx9n7CSHiz604HaRfgH5cQRMMHCoBgYeSSMnzpVsvTQUoYOmwJAEboplO/5oLGsxBXWvFoYcj3EdIAFC8D5jG7eDC8P/4Tr9/8fmJ8MTJm69FLVFErP3NoIH4saMZtJoICjpENFojMct05ZhehfHa022ka09fYLThIiEjh410EMisFnPimAy7Mu55we55C4RARF2JDjvS8VFzv3EgGhkAgLg8RkK2tH9sei7ODOPE17MnKk98LcNRZ6LgigQ5GBAxGwv3A/I9v7+K4X3l33LsfLjxNrjqHPvlL6HFV59t9fYTbWPo7HakEJyspd53rVWKiq2HXV8zMNE2H0Nx3dxO1zb8ehOvjPqv94/HZiRCJZ6Vn8sV8zDzZW0LXOVxV6Gjw7FBQUMG/ePD766CM++OAD59+Het50SY04BYssYYrz6aZPcagORrQbwdTeU2v8bldHJHQTsbIHDRLOoytXQoypnMv42CmsAGJ1NnOmWCGsXi3ibNcgVADM2z3PKVRcetKlXDvQuy1grMkGWd7bu1aNhRZmdskpnTlcmkt8eDwTu9YSIaEK1wz1HDTV7CEis6qusYhyCRZFliI+2Sg83f598r/9J1RANY3FhiMbnB9ZLGIgjwuQxoLwcM7jBw69Mx/N1Yl9++BGLXiXCaswXdm0Se9qHN8rJpSjZZ5CqO5XSOxh2thr7iMDIraRQD6FxUan/8atvE5CeP19rGK0cJR2TBhwcOutwilRl2XbtxfhdzNii7ESxqqDviflEouusWjc/e6RrIUuLo9m0iRhQqYncBMFCSc63WnR74KFvnC3u2nlVguzNzp39ntiPCeaYGGwWrhVxGzgnXeEA32bNAf/5SYRac1fC+CsLAawDhAai2hjDGM7aLuFmn9FnFIsEon6a/EZEUEPxz7xuqATiw8spkLbdUyzlgTEvwKA8HB65QHtlzHq9rdYuBBu/eY+GCz8y8btpfY00o1gVL7Yxl16ZLXzvSJNYxGrBE6wuMw4G4BzTvMSMU4XLAIkUAHVBIv0dHiy43/pwt7gmkJVVroGWefA4WfC3DQWxRkiLLYb5RZxvTHGupmsRpmjiDDV3jcSIhJITxI+e1uOVt+u/9//RLS9sjKhzDlxQkR5WrUKOt92K8eVHfRK6cXkpdr98qWR1QWLw4ddoeqBjsdFP9pfUL90B19vFYv91w72Y9kslXcODiJ5aN0SB8drodXLy11Cl1cfi2XLhCnBmjWsaAv3D8jjki8v4eR3TnaG1R7dfjQzxs1gxfUrWHvTWg7dfYiFVy/k5GQRtOdII017g02DRtAffviBDh06cNZZZ3Hbbbdxxx13ePxJakfRk9T174+qqk5fhiuyavcx6OyIh6yPPN/rDN+PfYG+bIErrhDGjGvWwAsveKZproXVOat54LcHALhrxF18csEnIsu2F6JUE4xx7fi4jwVOjYVuq1UVbVCYPUioMM/vfX6NuyLeGNM9lz10ZttkLRrEkSwO5uW5fCyitUWu2czHGz6m1FpKr5ReXs26GoXB4BrMKxJYm7vWaTdZWamFIVUDJ1gAYLHw4YdCjgQx7oLQVigAGzc6BYucPUngMFQzhXJm6o7IJ81Wc1sYw02MZ4Hz/8zkCp7gwQYtrA9YXZEuPrr4e157DbSgc04UBYZ1FJPy6sPVI5joFGsq8Zjw6uF368M/hy3nY/5OnLmM5cvh9deFKZRBMYCKMxa+M7xjoDQW7s+P7tBS9eb4E7f+dO21rh3mqVNhy6I8eqBlOvfX4jsri15sI0yppKhI4ciRKC7uc7H4TPOviNfD7vpRY9GJfeJ1QWf+3P8nlRWi37SxlQducR8eTk/NUnX7cZHUK88qHFAHHIY35iiBFSxKhYZ3Sf56VFWlwlbBH0ZRficSAlbuc+EP8h5X8ckL1cOfBkVjof+2e2IW/XWgTaHcNRb68xse7hI8/I3ZzCC0QC2/PsW2g573vEITLOIU/89HPZJF596Xl+k0CQKxYf/cc67/x48X46aiiOhvb68RgvVbZ79B2BbxXDgnq6roplAWi9h0aSPmju65oj1X5Kyoc313ndjFxqMbMSkmznn/LzGmvflmnZ/BBG1DrKLCNVd6NYVauZK1bWBTGoy/Cp62LWT25tkUWgoZlDGI3H/lsvjaxTx0ykMMbTuUAW0GEGUWAlR6hNhAahWCxb/+9S+uvfZaiouLKSgoID8/3/l3oqrqT1KN8IIClKNHRUfu25clB5ewLncd4cZw/ta3dqehLmoC9HAlKLz3XqGQGJe0XrwRFgZ33AEDB3r/AR8cLzvO6HdGszZ3LZGmSO4YXrOQGK2aIXk3l/x9Jm+8Ac884/osTN+hd65Wq7BqFXYFvowXtvzOxUR9MJvpzD56RB7EGHcUHGEsXl7hCjcbqfmARCjOjN43D77ZP34V7rhpLJTjfSgoKXXuFFkrxUIzgcYtdH3ithBs00bIke7pDZztsGkT3boJZZWl3AT5XaqZQjkng4hC0h21GHSGhXEarlwpD168S4TqbMBE3T1CLG7SOMIlk31rPAZ3FmPL6mPtvX7ucEBppVggNlZjYRw8gL/zKU9YpwFi1z4mLIbTOp8mEn1p5n8BFyzcNRZ6GWGBs4d39qeKChISRHC5L74Qf4mx2iLQYPDfAjgrCzM2TkJoHz/+uDeX9LyauZfN5cXh3wKQoWjmI/66txER9EbzcTs8kFc+3IPlhHAaz7QFMKqhrrEAtuVtA+BIiVj43bsUehYEcHENDLWlE26DbOtxthzbwicbP+GooYwOBXCW0j1g5cYbS7iKD7xnuQ6mKZRelvvrQESFAu8aCz3BQtu2gRMgzWZm8gAdDLugsBM7fzvZwznYom28xBv8v1AdkFYGaRux2cP4yG3fc9Mm2Ca6O5deCtOnuz57ednLqKic3f1sxtjaQkWFEB585diq6mhw/vkQHc1kTR6Zs3MORZaiWuuqqirT5ouxfZy9PUnlwJQp9fJdS9S0PpUVEc6w3FVNoQ4XH+azbV8w6GbodwuUakP306c/zftT3mfZdctIj/GtgU+PEr44R8JsPs9pijRIsMjOzub2228nqjl5kzQVsrNJ0c2guneHqCieWyrE+av6X+WRJ8IXnZUkMFUS//cLePhhV6Cnxg6WC/ctxGIXqvGl1y2lY0INaZ+BKEQ5XU9awM03wxhX2g0OIyZq5yrfHYcDli3jz45wxFFEYkQip3U5rf4V1q5TsVmJ7y4WJl9/Eu/080iOKOXl4RA34nc2Ht1IhCmCK/tfWf9yasNgYBhip0TdNwa+/x+rcoRGxqYJFklBECx07r3X9bERbTJfswajvdKZKIwjWdU0FgUF2u54REGdBItJ/EREmJ2uXeG6cSLca0MWCC90fpVbeJ0NZKF08t3nBncTE8bqE128fu4e3TgmopH3++KL4d57OU9zLN68Wcx5l/W7DMqFNGE0OVzzXDAFi0AthqBafxo61C36qq498afZSseOEBPDTeqbAPz5Zztef93IGd3OIG+f0ExlKZp2148ai/6sJ8ZQDJYE+PQH50dtrYGJ3qaXqwsWe/L3YLFZnEnM0ksInAmWRnR0Aqdrj+nszbOZuXgmALcvp84hvhuEN+2bTgh8LDxeB1Njka0FxAiUGRRAWBjxFHGDKsIiVR7M8kiUZ9U2XhIM/p+PekRkwuD/AiIdUWWlyH97883i8ylThImrPgfll+c7/UrvHnG3kEAA+vTx/SxUXXNOmACZmfTPhZ5RHaiwVfD99u9rrOee/D1M+HAC32z7hjBjGE/8YXRVsB6kaEkXVdXojI+zWQsI+eL6B7n868tp+0JbLk1f7PG9l854iWmjp3Fl/ytr9eVIixaCxdEIL0J5E6ZBI9kZZ5zBKjf7Nkkd+ec/MXfuzIDXXxf/DxnC9rztzgfh7pF31+lnOpuEeqywx9fc91CpK5x9IwfLBXuFacttQ29jQJsBtZ4fpTnaljnEIsRoFFGhEk1F3Ip2jd4Ei02boKCA2QPE96f2nkqYsQG7sG4TRpveYsZc8E1HCgrEhseAjCMs7OQ6/dKTLq0WMtcvGI0MZwWz0bQumy7hz+2bUFWwWcRAGHDBosK1IBo8WAzofdsVOpOdsXs3TJ3q0jAf6UeptZQyq8vu+XCe9hvhhaRRu2DRkQNsfmcFy5ZBuK5ab0DfGxq7jde5jXSOiogZPhjcS0gO20rbOVPAuKOnRYmgnIgwHyZ4dUVR4Pzzac9Bkg0nsNlEt53ae6rTDCohQXVtPAZDsKis9G8Z3vAiqDrR6+JPwcJggB49uJFZPHmFmJX/+EPcVH3/pZ+qvfCjYGHCzhjTcs/3zaX0rax9t7Mx5bYpgViHGYfqYHf+bqfGIr2UwPoZAMTGcr6mqJmxaAa7Tuwi3R7JDWsCXLa+SGxKgkWgy22vaVW3bHG9p2ssAilYaNc6RF0r/j880Kk9dzjAZhX+EolG/++A94hqD/0/xBBWyNatIprfmDHCx9poFHk33Jm1ZhZl1jKy0rNEyFtdsHDufnlBN4UCUYAmWCjAJZFC2/Deuvd8fr3cWs55n57Hb3t/w2ww89aIJxn6xy5RQS1tQl1JMlhFLg9EwJRNm0TUK8Vgp6jz+3y88WNUhCpDVzhEmiK5PKvuQWrSY4U240hkKxAsJk2axL333sv06dP56quv+P777z3+JD7QUvSa9EXgKafw4rIXUVE5t8e59EzpWcOXXSSExRGv/cTBIrckZ43UWCzYJwQLX3GtqxKtCmGg1OFahNx5J5wYM4U+uqmBN8Fi0SIsRvi8r1hA1MX8yytuE0aXLFe87qFDRSS6SKWCHC1U6dndz+blM19uWDm1oU2cF/MF7bvlgcPM/B/jWbVKxVGWBKYyepsOB6Zs3RG/ykJw9GjY9NSP3InbNf/0E1ldxeJcOToA8IwMlZsnhIxwUwHhtTnsaeY4XVKKhB9xY/reUTfNSUaGz9PSMwy05RAqBtau9fystNS1M3YL/0Ex+WGh1K0bCjDIITZR1qyB2PBY3hgvnFGTk9zKaKEaCw/0uvh7Z12LDXlab7HwWrZMweEA3Q0ty6E1th+dtwHGKEucb91x01Y+jxtBm0BtAACEh6MAvSqFU/qGIxs4Xi5MZdJLCIpgcd52MOAyw3mqeDhxAQxzC3gPRKCjvxcqwSJQz9LIkeK6d+1y+VYEQ7AwGMBgYCDaM3O8B5sO7QM8onSTZAiAYBHfBSKKUC68BJNJde6DXHONeJZPczNKqLBV8OqKVwHhy6koikuwqCl5r9ksTMkAvv9ejB3anHG1pTcGxcBve39j67GtXr/+wG8PsPnYZtKj09l661auytXMkIYPd2mZ6kg8JhjxkvMa9YApbYasAM3E++9Jp3L0GSj9oB2Lrl7EomsW1ckqRSc9RlzbkWi8Pz9NlAbNEDfccAMHDx7kscce46KLLmLKlCnOv/PPP9/fdWw5TJ7s8e+J4Vm8v/59AO4ZdU/dfyc8nDTN7MPDTr6BGotyazlXfHMF2/K2oaAwtg4xnAGiFDEol1V1THZ/ALwJFt99xw894YTZSru4dnUWZKqhJzjIy6NXvzJov5i2J+3il1+0aH5Wq1OwmD52OrHhsT5/qlG4TcoXnCckvl3vT2PYMDGBG3t8R3+L6vWrjaamhaA+eZ5xhnOnJ6ujMAI1aIKFuznUkeOiHWMMdcgZoJerzx6N0ZbpEy7UvMCJjGQkInb5r57h2VmyRFgaZMQUMYOH/bNQSkmB+HinM6SevD7DLPKAJLorv4IRFaqpCBb+XoRqC/2s1MNERNgoLFRYvlxENwPoh/81FgDjHa5O9PQNdi46vimwC1yt3MGlwrNz1ppZABgVI8nlBNwUirg4UsvgbcsZXNn/Sl6Y+AJXlmiRzgJZdqhNofTfdvexCLQpVEKCy8dRV6W6+1gEkrAw0jlKdOIJwMDSlWKxUOZSTpNk9P8itUN8B6Iqwd5jLh98e5A33oC1a4V/Wh8xZJJdlM2ZH51JyjMpHCo6RJuYNlx6kpb7bL3mI1qTxkJRhDp+61ZXZm7NoaFTiYlze5wLwL/m/YsT5Z7+vmsOr3EKM+9OflfkJdqxo/YyfRCrmmHYa5ii8qmsFJuZYWGQMkGYd71+9ut89IOJ1DIwTT6fMR3HMCRzSC2/6kl6vDAHPRKDa65tBjRoNHE4HD7/7M1Iqgo6unpU4zvHFmcCujEdxvj4khfCw0nVBYsyL4JFPRceLy9/mY82CG+rC/pcQFJk3ST3KC2pUjXBwn0AdxcsHA6xbfHrr7w3QLx1ZdaVGA0NXKjoYXU3bKBNTAJcN4axjz7iXPA5Ki0c1mSJtnEBHMzdJuU7LotCMXs6gPZN/4QwQ4AWgzUtBN37gxamol+m2CG1H+8ElmiP/nM8X7RbAgW19yHdgVgf7BqzQKioo117VBTnIuzhqypG9Tnp5Lb7iPJXZmhFge7dGYwIz6kLFrqTe0AFC2+LsRYuWJhsFXTvLqKrXam5QrVrq5KkR1zzs2Ax3LqYTz9RWb4cwg0BXmi6lXvdUTEW6aanqeGJGFSCorEAuCa/E+9PeZ+7Rt6FwR4Av5mqtEZTKHAtfBcuFMdg+FiA83o7dhWbRhvWibZ1ChamcrHb7mcMsXH01qb7iC6ruflmGDDA85yXlr3EL7t/odRaSkxYDLMvnC2iQR45ImJag9Ae1ESnTm4JhHCFrisp4d5R92JQDPy862d6v96bH7aL+aLMWsa1312LQ3Xwt75/46zuZ4nv7NSi3LklKq4rsWoYRBTR8abzuO8++Ne/hPlmefpCALKKo1Dm/yr6vx6usZ44BYtoPPtvE6fR2xQVdV0USACw3y38KBynnMJXWgzli/pcVL9IRWFhpGqDhIfGogHq3SJLEc8ufRaANya9wecXfl7n70ZrztulapVFiLtwmZMj4s29/z688QYsWECZGX7tJrreZVmX1bm8anTtKoJjV1SQmi8WuO7346i1ALsBDKridIIKCG6CRae2Vu7+7FW4fhhkroDMFZxn/yVwE3ddNBYmk1OwSDUc1yP0wdGTPDQW+QVa9mG1oOGCRSAXvZGRnM0cDNhZv961ow2wbp049k/RJm9/7cB260ZfhO3/9u2iK+dr61yPJEityRTK331Z06YpFRWcdJJYmezSwu+fPdFtk8LPplAAl1xgFfmqgrHQ1Modkh/J0ExX9Jn0cM00IkiChYeDkr7YD6TGoiZTqFALFoF8lnTB4qefRHnBMIUC5zUN6y/8hXYsHEJFhVtQD3MZcWoAIstFR9NHm363HNtS7WOH6uCzzZ8BMLbjWJZeu9QV+n3RInHMyqp/KF49zXZpKaM7jGbxNYvpk9qHo6VHufCLC1l+aDmXf30564+sJzUqlRfOeMH1XV1j0b3+UdFitY3VyqQtPPWUCKnbo4cr0lvaMk3Tes45vqNc1UJavNiEKAuDCS/2J0wza2vqNGg0sdvtzJgxg7Zt2xITE8OePcJx9uGHH+Z///ufXyvY0nDMmMG6W24h/3//Yf6e+QC1JsSrRm0ai3oM0h+u/5AT5SfomdzTZ4ZtX0Qpmsaiql2yu8bi7bdh1iy4+mqn99affx+NxeCgfVx7eqf0rnN51TAYnBmIU/eLBYn7/cixCVVouiMysFFP9LoAOBxcc/IkaLcSbhgONw5n3EFraAULN40FhYVOtTQnunO42OX7UVwo2j7VXhhcjYVODf4VAERGksJxRmtOt7NmuT7SNRYDUrTJ21/3u3t3urAHBQdFRUIBpwsWHhoLfy9UmqJgEYioUOBa6JeXM2XKLmbOtHPmmSJZ7UtPuIX68td1O6Nd4NKWBWOB63Zv3QN1mBWtzECbQnkTLAIlLLoTalOoUESFAjj9dGFOmZ0NH37o0ljUEKDCL2hj823nl4DRQvm+AcTGqnzxhfZ5h8XEUr+cUXUiOpq+2j7VlrzqgsWSA0s4VHSIuPA45l4+l37p/Vwf6uZiY+tmhl21XABKRFLeke1HsubGNZzZ7Uwq7ZWM+N8Ivtn2DUbFyGcXfkZmrJYHSVUbp7HQ7mGxw7W5Xm4tp7hSPF/pK7c2/Jo0YiLinK//MGfzXfYXNZzddGjQSPbEE0/w3nvv8cwzzxDmFlO9X79+vP32236rXIvEbGb/xIn8Vr6ZSnsl3ZO60ye1T+3fcyc83Kmx8AgZ2oDFzedbhIbipsE31dskKdogHqzSmkyhSkrgpps8Pp4/UCxyJ3SZ0PicEkOEzWLqduHE7q6xyLEVAJDp8J7gz6+4LQT7pvXllTNfITosmk5haYw4RGgEC/fJ002wcK7fS9I5VOTybygpFv2mja2g/oJFYybqkSPF8Z//rPk8bWf7DkXYyT73nNhwqqhwxUnvr2V/9dv97taNCCy0jxD9audOH4JFS9FY+AgG4FGXQAkWFRWEhzv4178c/PwzPPAARJrdxhJ/lesuWOjXGWTB4uK+7nl7NP+rUGosQm0KFcTM2x7lBlpDpc99d90lrr9LF1wq4wChXe+gtFhM/UWgCZtNIS4OYv52GfxtKrFKAASLmBifGgu7w+5MvDu199Tq2bwbI1i4aSx0wk3hzDp3FsmRQvvRLakbC69e6OnPeeSIeBYMBtEu9UTX+hSrFc5cIfp6LMwYRtxSLXKqPr81AEVR6KdFCz53O0zq3AgLjyDSIMHigw8+4K233uKyyy7D6DYgZGVlsU2f4SU1snD/QgAmdp1Y/8W1nzQWOcU5/Ln/TwAu7HNh/eoARGmDUxk+nLffeKPad1RgrkFouCZ2nVjvMquhayw2it88VnYMh+oQGcQNwoa5LQFy2nanyuT5z+H/5Oi9R9nQ9XmirAResPBmkuhDY5Gu5+MpaeMRVayiRPxWZmUdnLf9aQr144/CacI9AYc3NMFiqvUzzjxDRB3p3Vu8bbeLoB5tI7SEVP6635q9U7ewA4Aw0WnRgkUITaFq7MNatBu/YDC47mGIBAuDYmDZdcsYmjmUp3reKt4PlmBR5BZSN1CRvtwJtSmUN+ftYJhCgQhVZzS6MqeNb2Cgkvqg53eyWulzyccwaBaTr9nJ8uVQ2Xs2GBzEBUhjoQsW2/K2YbW7BLm3Vr/FkoNLiAmL4ZGxj3h+LztbRIRSlMZpLNwTGQHt4tqx/bbt7LtjHztu28HJHU72/J6urejY0XOzoY7owpkdB+VaYk1dsEgPS0I5eky0RT0TFVdl9hfw9Wfw3afgSMts1G8FiwYnyOvWrVu19x0OB9Zm5GASSn7f9zuAyORbX9w0Fo3xsXhi0ROoqIxqP4r28d6zGdeEyxSqSug6vR69e8OoUa73H3qIBW9OY/OJbYQbwzm9y+n1LrMaacJ3IvNYBVHmKCrtlWzL28bUz6eyySDMozKJq+kX/IOXhWCUOcpphxmwRUNNO8w+NBbODbNST42FtVTkrmhvKQiuKVRSEpx7bu3f1W3xgdeeq8Bg8NwEPe88UBx+XvxqZXYz7Qc8BYuA+ljUFBUqSJm3qxGoRaibxqIagVp4Vr3OYAoWWpnD2w1nxQ0rOC1JixQjTaECQ6hMoUD4U1xwgev/ceMCWx64xgerlX5dU+G8Gxl+zZd07mahUkuYGqvUEk68IcTE0LkA0kpEONl5u+cBIsv16ytFXqsZ42bQKaGT5/fmzhXHoUOF6VgDygWcplDuJEcl0zGho/fNW91+tgH+FQDRSjgpmizzy65fAJzJCNN2a+HvBw708OlqCL3z4PxtiPVUM6FBI1nfvn35888/q73/xRdfMLCR0llr4IT1BNuO1y+0qwd+0Fh8svET/rPqPwDcP/r++tcBN1MoXxoLkwneegtSU2HaNJgxg8fClwFw4+Ab6xXP2SfaZG22WBnWdhggMogfKDzgPCXeEOn1q37Fl7o/0BN3A3wsnIKFm8aishIcWvKkTg0RLIIxUbslR+qaUcYTT4jXY8eKzad33sH/91ubFLobRDr3XbtkVKiQaCz83a+qCuRB1lh4EIzFPYTeebspCRbBKFfn9ttdr3WH7kDidr19U0UY1V/3/sqve1zhlQNiChURgQGFS7V0FDf8cAPvr3uf99a9x+Zjm4kwRXD1gKurf+/nn8XxrLMaVq4PjUWtvC9C/TNhQoOKNRhNIrkk8NLylwA3jYUu41x0UYN+2xuqZp3RHGjQU/XII49wxRVXkJ2djcPh4Ouvv2b79u188MEH/Pjjj/6uY4tjXfE6AAZmDKxzaFcPfGks6rjwUFWVpxY/BcBDYx7i3J7n1r8OQJQmWJQpPjQWRqOID60lQNubv5dF+xdhVIzcN/q+BpVZDbcF7uj2o1m4byHvrnvX45SuxgbsgtQXb6Yr7v83IR8Ld1OovLI8KmwVFBWEg5Y4q0tpUd0Fi6oLs0Auek0m8ftWK5SXc//9cP75YsPJuS7y9/3WNRaqUJtv3+5a/0pTKD+hLfIV9wxeOoESWKteZ2sVLIJRdqhNoUIVFUpn1Ch4+mmIioLMIJiy6NdUWcmlQy7l/xb+Hwv2LnCGNwYwGgNw3YoCMTFcub6Yl0fA4ZLDXP3d1c6Pz+91PgkRCZ7fsdlgvghi02DBogaNhU/WroVVq8Q8dvXVtZ7uFaORW1bCs2MMLNq/iM1HN7siQpUifAbvqUd+slpw/P3vzSaXRYO2Kc4991xmz57NnDlzUBSF//u//2Pr1q388MMPTGig9NeaWFYgdu3P6X5Ow37ATWORV5bndByq62C57NAyNh7dSIQpwiM6SX3RBYtyxYZDdduN8pFN9ZfdQl04qv0o/+WVcFvg6jaUq3KE01SMw8zDf8AVpiBI+s1RY1EqXhwqOkT2Ma1DhRWRWeYITVSouqDvbmuL0J49q2y2+tsZVCuvh0P4jq1ZA1s0v8RWJ1gEOiqUN41FoK45lKZQVe9tMByoQYTmBrEA0/uVfn9DYQplt7vqEegNCXC1sd0uIgK5fxZIFEVo7LWoiAHHzRSqU0InrhlwjcfHXU6AGqj2jo5m4GE4t80pJEcmM67TOLoldaNtbFvvSYA3bRI+P7GxzkAsDSkTqLvGwm6HO+8Ur6dObZj5FYDJRLsiOMvQE4Avtnzh0liU4jTTbjSbN8N336Ge7gfT8SDR4KfqjDPO4IwzzvBnXVo8m45uYuafM1lRtAJoQJhZHTeNhdVhpdBSKHYC6ri79/YaEbnrb33/RmJkYo3n1kSiMZowG1SaYF/BProkapEVfCzu5u4StpRndPVjv3HLAD2y3UgUFFQtysqlJZ147PedcFaU/8rzRW0ai0CZGjRGY1GWDHYTh4oOceJwGBCDElFIVBH1d94Ols1yZKSYiLztbkPATKF6V67nnnvg1VfFrTYYqgR3aQ2CRShMoQJ1zU3RFCrQPhZJSeL6bDbIzRUZoPWEIR07Bq5cX5sux7VAC4pSRUr3M1U1Fu5O3MHQWASbKtf7xPgnsDvsnN7ldCxffkbPr7+HcwLUz2NiUIDvT3oSRo+u/fy//hLH4cMbPq5UCTdbI4WF8I9/iLwZMTEwY0bDygRnfS9STuIHtvL55s/JiBUhFzOK8V/f6tNH/DUj/+UGjWRdunThuD4ouFFQUECXBoTtai28uvxVPtn0CQBdErqQlZ7VsB8KDyfCBjFWYbriNIeqg8ai0l7J19tEYj6v9o71wGwM4yQt2u3aw2tdH3iZpCvtlU5V7Bnd/ChYuC1w4yPimdxrsvOjvuWa6j8Yk0dT11jonsaFhaSkaNVVDVCayr6CffznDxEf2xxe5PpOTYQiQR5U01hUI0CmUEpFOc8+CwcOiGBnn39eJe1GMAQL/V63NMGiLhqLlmAK5SvQQrBMoUwmaK8F6di3T9xvXbDo2zdw5foyhcrVHFyTk4NrCuUuWARDYxFsdNMgzRksNTqV/03+H5f2u5Sr7f0YGcjw5/VZ5AMsE9YbjQnJ6hFuVtdE+eLOO+HTT4Uw+9//gpcgRHVGu4fnqT0IM4axNW8rC/YuQFFh8nZaZt+qIw0SLPbt24fdi72kxWIhW08C40f27dvHddddR+fOnYmMjKRr16488sgjVDYTezOdy7Mu5/ye5xOmhHH7sNsbnsNBmxTTy0TzZRdr97wOk/Bve36joKKA9Oh0xnQY07DydQwGBmpzw9pcN8HCiynUpxs/pbiymDYxbRiUMahx5bpTZYH78CkPOz/qU6YNcsEQLELlY9GAqFBGo5uWtjSdZ5c+y2+fi8gYbVK1cNFN3RSqrMz75wESLLDbwWolLU1EkHQP9AK4FkkeoaIagV5/VXVNlsHQWOg7x3l51T8LZVSolmQKVVnpatPKpjh1awAARGBJREFUSrHAgcALFgCdOonjvn0i+YvDITQZgcyt4GvT5YgWoN+pQg0QVQUL953flrj407M8791b/bNAz0deckrUiK6xGDGi4WXqwozD4X0e1HE44IcfxOuvv4a//73hZYLzHsY7zFyZdaXz7bNKM+hUQMvUhtWRej1V33//vfP1L7/8Qry+WEFk4/7tt9/opA9cfmTbtm04HA7++9//0q1bNzZt2sQNN9xAaWkpzz33nN/LCxRjOo5hROYI5syZw9lDzm74D2kTVL88A7vj7aw5vIZTO51abRJ2qA5sDhuzVs9i49GNJEUmMWfnHEDkrahvQrxqGI0M1BI3ewgWVUyh7A47Ty5+EoC7RtyFQfHjwkSfrG02cDgYlDGIB05+gA1HNnDKOk1wC8bkEWqNRT3yWICYy3NzgZI2bFl/FHach2Jw8H2nP2ALTTMqFARfY+EeKrCiwvd92bBBHLMaqIWsivvi3W4X9zUYgoW+o11YKJx8dYdfvR4QOFOompy3W5IpFIjnJjwcvv0WvvxSvOfNudnfuAsWunBz0kliBzdQ+PKxCJVg0dI1FrrVyJ491T8L9HxUH41FXp4rl4Q/BAu9XF/hXTdtEuZ30dEwaVLDy9Nx20x89exX2Z2/m4X7FnLPkW7AYSlY1JUpU6YAIhvgVVdd5fGZ2WymU6dOPP/8836rnM6ZZ57JmWee6fy/S5cubN++nTfeeKNZCRZ+Q5ughmUrfNsVVmQLn41SKnlvKFxsLcRanMPQWUPJKc6p/nVjODcOvrHx9TAaGaQLFm6mUDaHjf2J0FUbtH/b+xs7ju8gMSKRfwz5R+PLdcc9pn9lJURE8ORpQojhrVOrnxMomlFUKBAblOvXAwWdIHcAAMPG59I/QtN+NXNTKL85J7pPUuXlngttHVX1v2DhXn99QRYMwSI2VvSVwkI4eFDY9uoEOipUa3HeBvG8hofD7t2u9zZuDFzZOvpu9r59ruhQgTSDAt+mULpgEehM1FWdtwORdLEpUZNg4X7tgaA+Govly8WxZ0+hNWsoRqMYQyoqRLm+nLF/F7nDGDPGP+OJ25wfYYpg/hXzyS3Jpe3l2hpHChZ1w6FNcJ07d2blypWkNNSb3g8UFhaSVEtntFgsWNwWXEVaxlGr1RqyRH56uY0q32DADAw9KAbqldkrsVZW8uoQBw+cDk99eyYX9L3YKVQkRSZx2UmXcbxc+MVMGzmN3km9G30PDKpK1hFQVBFa7lD+IdJj0nl5oIV7x8NLW9/nlvYP8eVmsSN3Ye8LiTBE+PfeKwr642stLXU97Bs2YP7jD1RFwTZ4cKMcn+rSZiaDAQWwVVaiup1nqKzECDgUBXsg+pzRiBlQLRZsVX7fWFmJAbAbDDiiosR9Ki3FWl7O4MFh/PKLERZOhzQReHzsyCgcaywYAJuieFxHVQxGo7iuigrsVqurLEXBEcBnyxgRIepXXOy1fkabTdRD+98ffc0UEYFSUYG1qMi7k+n+/ZgLC1HNZmxdu/rHyc7hcPXrigpQFOc9rq1tGoupXTuUwkJse/eiuiWPUiorMQEOg8GvfVkxmzEBqrbId28zpbxcfGYyVevfjcEYFibuZVkZqtWKwWIJ7HMKnmNVSQlERmIwm9HFNMf55weubL0K7dqJNtyzB3JyxLPSu3ejntnaxkejooh7XXVszMnBCNhTUwM6ZgBijLRaRR8qKxP/+7lPNRnatxfXt2dPteszWK2in2uChb/XQcbISNGnCgtrbVPD4sWiLsOHN7rfm2JixBidn+8zpK/xt99E3U45xS/9zaAoov9aLM7fS4tMwxGgcdova0c/lF8XGrQ9s9eb7V4Q2b17N6+++mqt2pGZM2fy6KOPVnt/3rx5REUFIVJQDczXYzc3gLDCQs4ChmiCxZ6CPcz+5mO+6SU+P1R6mJdXvAzATe1uYnzSeMKt4c7W3rdyH/vY14jaC7rs2EE/K3Qti2RXdDmzfpzFoLhB3Dte7Ircueox2lv788Vm4RicWZjJnDlzGl2uBw4Hurv2rz/9RKW2Mz/4+edpB2SPHs3qvXu925vWk5ra7LSKCmKApYsXk+9mn95182ZOArJzc1nj72sHYvfvZzxQWVzM3Cq/P/jAAdoBW3bsYO/SpZynvT//q6/o3z+eDh3GcuBAOuwVpghmxzaOHT5MOrBhyxYO1lDfTjt20B/IPXiQlXPmMGjfPtoDW3ftYncArlNnWHExGcCmlSvZ72WRPyI3l3Rg89atkJnZqOdM5yyjkTBg0bx5lLStHiY5fcUKRgBFbduy8Ndfq33eEIwWC3ow6l/mzMEeGcmYY8dIAlZv2ECuW7JAfzMiPJx0YOPPP3PAzWwkY+VKhgH5RUUs9mMbx+/axamARUtp7t5m6StXMgIoKCtjkR/LHHLiBG2BLWvWsHfOHDqvX08WcPjYMVYFsP+eazJhsNlYMGcOFampdNu4kb5AUYcOLJ46FWsAywZIOnyYMUDFhg2Yy8owAEsqKsj3Q7m+nrWTCwtJBtasWsVht13cgWvX0gHYlp/PrgBed+zBg2KMLC1l7pw5RB05wgTEhovf56MmgKmsjEmAkpfHvC+/xOa21um3Zw9dgD3798Po0X4ZH93JOn6czsDOdevYXsu9HfXTT6QCG6Kj2d/IdpigKEQBS+fPp2D/fq/nnLF4MRHAEoPBL/29z/79dAf27trFZrffG3X4MKnAus2byQ5A//J3m9WVMl9+jV5osN73t99+47fffuPo0aNOTYbOO++8U6ffmD59uteFvzsrV65kiFt845ycHM4880wuuugirr/++hq/+8ADD3D33a48DUVFRbRv356JEycSp8f0DjJWq5X58+czYcIEzA1VlWmal4QK6JHYnR35OylrV8LRaM/TBrUZxCtXvNJwJ/FaMGiq1iHWFHZxEHMHM2ePOpv2f8BBzfLmSOYRCtYXEB8ez70X3UuY0f9mSarJhGKzcfopp4gQioDpYeHE3ebeezm7oYl3NOrSZiZNBTxq+HBUtzB7hs2bAWjboQNtzm6EX40vNBvVMFXl7Cq/b/zgAwD69O9Pr/POQ42MRCkvZ8KwYdC5MzabwmWXuc6/5prBpC4Ti/WswYPpV0N9FS3pYZukJM4++2yMn34KQO9+/egZiOvUMH78MaxYQb9u3ejrpRzja68B0Dcri4PQuOdMwxQXB6WlnDJ0KAwYUO1zw7p1AMSOGlWtDRqMm6b1jNNPh/h4TNpYOXjECNRG9umaMPz4I6xZQ1ZiIie5XY+iTSyJKSn+u04QcdqBCG2ccm8zRTO1i/dzmcYvvoAlS+jbtSu9zz4bg/YcZbRv799rq4ISEQElJYwfPRq6dXP2nZgJE5jwt78FrFwn/frBgw8SpW1+qOnpjLz99kaZxtQ2Phq1DcBBAwagut1b43/+A0DPU06hRwDvuXOMVBTRttr/xvDwgLZ1KFFvvx0lL4+J3bp5jFkGbaHbuXt3tuGf8dEdw6JFMHcu3TMz6VrTvbXbMV1+OQB9r7uOvo00ITWlpMCxY4zu3x913LjqJxw+jDk/H9VgYOTNN4tkhY3EsGQJAJ07dqSje7/W+vuAoUPp78f+5Ze1YyPQLX7qQoMEi0cffZTHHnuMIUOGkJGR0eCF62233cYll1xS4znuzuA5OTmMGzeOkSNH8tZbb9X6++Hh4YS727VqmM3mkDSM3+rg5qx0de9L+ffSx7jp19tB28B99+y3CIuI5rTOpxEWSP8Crf6DKhL4jINsOLoBs9FIhJtv3C0/3wLAdQOvIzoi2tuvNJ6wMLDZMKuqy65R22k1xcX5zdaxxjbT7HhNiuJZnvZsGMxmDIHoc5pAo1gs1eum2TQbw8Mxms3C9vTgQcyFhWA2U3XM69LFjKLft8jImu+btmNusFrFdWmbC86yAoXW942Vld7L0euh9Xu/POvatZpttur3xGqFDz8EwDBqlP/a2G1MNRsMoty6tk1j0XIaGHNyPO+x3pdNJv/2ZX2TR/Ob8WgzzcHYEBbm3zK1hYXRZvO4Rr+XU5XwcCgpwezQklBq/dUQERHYcnU6dhTXrgmJyrnnYvYyRzYEn8+aZp5abWzUNidMmZmB7c96yGibzaN+iskU8nVAwOjSBfLyMB88CEOHut7Xnie9z/t9LaQ9y8by8prngQ0bhKN1TAzmAQMa77el+b6ZLBbvfUnzX1J698bsFnSoUWhzjFFVPa81wON0qNav9SmzQYLFm2++yXvvvccVV1zRkK87SUlJqbOfRnZ2NuPGjWPw4MG8++67GFqi01VdcRMW7u13Ez8e/JWlB5cC0Ckfrh54TXCiXWiDwcAy8aCuzV0Ldju5MZ6nndntTJ46/anA1SMsTEyU7uGHg+Ho6k6ookK5R7dRVc/oLlUdqtPShEOuNqFXjYyqKNQ9ulOoo0IFK9ws1BwO9cMPhQNuaipcfbX/yqwaFQqC16fbtRPHgwc93w9FuNlAO28HMyqUt3KDkZvEHaMRZs6EO+4Q/0+eXPP5/ioTml5UqJYqVICI/rVihUi8446P5LV+o65Rob75RhwnTPBPXWord80acRzkx1D3vgK2BHvt0QRp0ChaWVnJqFGj/F0Xn+Tk5HDqqafSoUMHnnvuOY4dO+b8rE2gI0o0RfQdTKsVk9XOf87+DwP+OwCAKCvBiYeOq5yBJWK3YNeJXeQWHKJYmzv/N/F1OrXpxdiOYxsf2rYmvEVG0ifsYESEgtBHhQJXCEudqot9PXmFPqEDTz8N990H06ZV+U4zjwrl1/tdU5m65vTeez3DHjYW98V71ahQge7TeshZX4JFgMLNKnY7SrAm6VBEhXIvt6pgEaxxCuD228WuwtatEECTOifeNl0cDucGR1AFC1UNXluHEn3XSI/8pdMU8lioqivE8oUXBqfc1avFMZiCRUvuX7XQoCu//vrr+eSTT3j44YdrP9kPzJs3j127drFr1y7a6btpGmptmRZbKhERogNXVNC/fX8u6nwOX+z9kevXGwIbk9wd7cFKrjTSKaET+wr28d4GYdcfaYVr+l+NEgwn+aqLXGg9GgtvISx1vGkswDWhI9bDo0e7jbeNFSxaWh6LmsrMzhYhExUFGqm99YrRKK4n2BoLX4KF3rcDlXkbYeLmQaAm6VDksfBWbqh2N6+8svZz/IW3cLM5OeJ/RXHL1hkg3O+tlugSaNkLP32hXXUH30vyWr9SF43FkiWwfbuYQ845x/d5DSnXl2ARTI1Fa9CI1UKDeldFRQVvvfUWv/76K1lZWdVsr1544QW/VE7n6quv5mp/mhm0BGJjxW6EtiPx0ejnuf6xHzk1N4g7X26L6SuzruSxRY/xwOLpAKSXCBvWoOBNsGhqGotAme65X1/VXBZVJ1B9Z9BNsFAUIVg4qeugqC+Q9MV2S02QB9WvVefbb8Vx5MjAxOIPlWChh2ssLfVMkhcoIdlNGDZUFSwCnXm7qZhCBVNjEWy8mULp0XKGDAl8f3b/fau1dSz8ahMsAm0K5WuBf+gQaPnQuPBCl39VY/F1vQDHjrk2SbwE32gw0hTKJw0aRTds2MAArYE2bdrkz/pI6oo+2WuCRZhDYeJuIC6IE5Tbg3XL0Ft4aslTVNrFRNmmhODtCHkzhWotGgs3s7hqgkUdNBbVqOt900O9njjhvaxAoWvAQqGxqOoD8NNP4nj++f4ry52qE1ew+nRMjGvj4vDhwAsWBoN4hi2W6oJFoDNvh9oUqjUsQryNjbqNfaCeHXfc29Rmax2mUPpCu6mZQn3xhch+3a8fvPmm/8qtSaBZqyXv7d7df4IMuO6heyZ3aB3PdC006Mn6Xc9gKAkdVQSLkKh33RY+6THpnN/rfGZvng1ogkWwHOybg8YikH4v4eHeBQtfPhZ1ESxq60fJyeJ4/Hhw7Zbro7Go2hb+LlP3VQlU5uKqfSqYjr4ZGWJsycmBHj086xGIvhwRARaLb1OolqKx0AVjfQHUGjQW+jxgtcK778KwYfDbb+K9YAgWVTUW0hQqdM7b27aJ43nnudYw/kDXxm/ZUv0z3Qxq8GD/lQdSY1ED9Xqypk6dWus5iqLw1VdfNbhCkjpSVbAIhXq3yk7UuT3OdQoW6WVB8vOA1u1jAWJRVlLiW2NRg/N2Nep63/RoblarKLs1Chb6/4FKVlfVNj2YfTojA3bsEBoLnUCa9UVEQGEhxqrZXVua87Y+busLr2A55IcSfex77TVwt3Do3h169Qpe+SBNobRrV0Olsdi+XRz93e5nnQX//jfMmyciBrr7dgbCcRtcY4UULKpRr1E03l/xfyWNp4lpLECEldWpNIVQsHA4XA97MMM4Qug0FuBbYxEIU6ioKLEYrKiAvLzgDaZNKdysLli4OR/7lVCZQoEQLMC7YBGIvqy1a9A0FqFy3q5qohLscLOhQBdEq5pNT5wYnPL1/Bm6tqI1aCyqCrA6odZY6IJFz57+Lbd/fxFid98+IVxMmQJ79wrTp0AJFlJj4ZN6PVnvvvtuoOohqS9NQWNR5cFKjkp2ftS1KEghb8G33TIEbycwlBqLqruvOlUXS7q6+NgxUU9vO8/1GRSTk0VkpOPHm6bGItBlBlpj4e706m5uFoxnXHfgDpZgoS30g+ZjESpTKF8bQi1ZY+FLw3X66cGrg7tg0Zp8LIItWLhrLKrmVSoshNxc8drfgoWiCLO6F1+ETz8Vm1033eSaj8PDpSlUEGnFWeaaOb4mqFCYQrk9WFsmzeHhP+BfawK0i+sNX8naoHVrLKouRHXzJbsd8vO9/1Z9Fq/ufhatUbDQBblACxZ2u6eDYDA1Fjk5rvcCFW4WPDK5e9BSTaFak8bCW38xGODUU4NXB71d3Z23W/I9D7VgoarVzaF0bUVGhn+dqHWuukocP/8cbrjBc5PvxhurZ4RtLFKw8IkULJorvjQWoTCFcnuAe0d34rHfIUIJ4kNVVbBw3/VsTRqL2py3w8Jc0Zx8mUPVx0zAXbAItilUKMLN+jKFCoZgEWxhOdimUNo9NtZmzufn8kLuY9GanLfd/z/9dP8v9GrCPUleazCFCqXztu7foGsndHTH7UD51fTvD6ed5vp/5EhXxDlnBlg/IhPk+UQKFs2VpqCx8PZgBToBjzdqMoUKchbykGgsfOVZ8LYzl5oqjm7Z6z2orykUtE6Nhd3uWhQGw8eitQgWvjQW/u5X+uJH99UJlY9Fa9jddBcsbroJNm6Ezz4Lbh3cBYvWbAqlX3ug5iNFcSXYPHTI87MVK8SxT5/AlA0wfbpo13PPhT//hKVLRUK+KomV/YK3OV9Vg+/f2QRpwU9WC6cpaCy8ZVQN9MDlDV8ai7Cw4GUh96WxCKT5iI6uhSgo8Hzf26LMl1MfiEGxPgsd3bQqLy/44TprESz8GvXEm2Dhrr1oiRqLYPtYBNt5u2r0mlCbQrUWjUXnzoFdWPrCm2DRkhd+ev8uLxfXWzWCUSDno3bthNmTu2ChqvD99+L1mWd6/54/OPlkoY2Pjxf9bvjwwJXlLY9FsE1WmyhSY9FcaUoaC/fFdCg0Fr58LEKtvXH/P5ADeVKSOOrJ6nS8TaA1JRJyr3t9NRZNzRTKnyFRvZlCuZcfjHCz+v1VlOAI7brGorDQtasf6HCzBDHzdtXoNaEON9uSFyHu/bVjx9DUwd3HojWYquiCBXiO9cESLMBTsFi/XmS/joz0NFcKBImJwcmh5W3OD4V/ZxNEChbNlSYYbhZoGhqLUERaqc3HIpADnS/Bwluf8KUih/rvtoTSFKqiovq9huCZQulChtkcuL7uLrgHu0/HxbnK1/tVMKJCBct525fGItDjVmvXWIRKsGhtplDh4a6+7D7WB2PjTxcsDh50vffjj+I4cWLgNmKCjbc8FlKwAKRg0XxpCuFmvZlCNQUfi1BEWmkKGovjxz3f99YnahIs3AfF+jpvB1uwgOrO1BCY/udNsAi04zZ4N4UKVp9WFJeJnR5BLAhRoYLmvK1rLKxWMV6E2seiJQsWqup63ZQEi5a88FMU72avodJYrFwpjuPHB67cYCM1Fj6RgkVzpamaQjUljUUIs5A7CcZAri/wG6uxqO+gGEpTKPBuDhXIqFDeBItAOW5DaAULqC5YhNJ5O1CCBQitRah9LFryIsQ9OlCbNqGpQ2uLCgXex/pQmkIBDBgQuHKDTU2ChcEQHHOsJkoLf7JaME3BebupRIWqyXk7WDQFjYW7YOErOoU/NRahcN42mcSfzRY8waI1aiwguIKFnsciWM7bYWGupGmhECyq+li0ZI2Fu0lMqBZbev9ZsEAkUIPWKVi4b/xVFeL9RdWoUIWFsH+/eN2vX2DKDAU1CRYteaOgDrTwJ6sF05Q0FqH2sfAVbra1aCy8mUK5+0u4T6BVnVbdcd/Jq0s0rQ4dxPHQIZe5QyB38HUiI0W/D5Zg4S1PSKCT44Hn8xWKne1QaCyCJViAeBYKCoRgEawNEXfBQlVbh8aiatjRUKDf32eecb3n7uDcEgm1xuLoUTFmbtwo/m/f3jWmtASkYOETKVg0V/QJqqzMMzNvKMLNejOFkhoLz/+DrbHw5Yhd1WnVnfraHrdpA+npcOSI+D893aXFCCRRUaEXLIKhsfAWFaopCBaBjAoVLFMoEM9CQYFYeAXbx8LhEGN3a9BY6P0nlFx7rUtzMmAApKXBP/8Z0ioFnFAJFklJYowuK4OnnoK33xbvZ2UFrsxQUJNg0dK1YbXQeo3Amju6YAFi4GgqGoumYArV2jQW3nwsfJk11cUUqq5tpygwcKDr/2HDgpM3pKaQs4EULNx304PpY+EeFaopCBYtIY8FeIZeDpZgER3tekaKi1uHxuLJJ8Vx+vTQ1eG66+DAAfH3/fdisdu9e+jqEwxqEiwC2c8VRQhyINpc11i1VMHCfRNPaiwAKVg0X8LDXYNDcXHrDjdbdeEXil3ApqCxcF+o1KaxqEmwqM+g6C5YDB1a9+81Bl2w0PMruNOSNBZNzccikFGhajOFCsS4FgrBQlE8n8HWEG72/vthxw74v/8LdU1aF1UjkEFw5iOAJ55wmcqC0BD97W+BLTPY1LT+kYKFpFniHk6uuLh1h5vVJ+WmEG42FBqL+HjXLqi+CHTXWLiXXRcfi/rct0GDXK+HDav79xqDL42F+71vaT4WTUGwaEl5LMBzgR9ME0593C4q8h5goaWhKEI7EAxtpsRFDc7baqAFi7g4WLECli4V4/KRI9C/f2DLDDY15bFoyc9zHZCCRXMmLk4cCwtluFloGgnyQqGxMBohIUG81s2h3BdK7hN6XRLk1Wdx5S5YDBlS9+81Bl+Chfu9D5RgoTupt0aNRTBMoarmsQjkhkkoNBbgegbdTRdbssZCEhpq0lgEIzpXejqMHNlyBUrpvO2T1u1h0txp106Ecdu3T4abherO26EWsiB4qufkZLEA1BcrvkxIanLebsig2KWLcNCLjnb5egSaUAkWIO5RWJjMY+FvQqGxcNfehUJjIQULSSDRNx5DYQrVGpCChU+kYNGc6dMHliyBLVtCo1L3tkvf2sPNhkJjAdVDzvra6fW3xgLgvvvqd35jCbZg4b7os1g8BYtAaiz0he+JE5CaKl43BcEiELudoXDedheyQy1YtPKFiCQA6IJFUZHrPSlY+A8pWPhEmkI1Z/r0EcctW0KrsXDfpW9KGotQOG9X1Vjo/wda9VxVsPClsajJx6K5OJ5FRYljVa1LMDQWuvAaDB8L3SZ59erQaiwKCsSxJeaxgNAJFvqzqihyoSfxPzUJFq08HKpfkIKFT6Rg0Zzp3Vsct2xpOuFmm5KPRWvSWHTsKI67doljXTQWur+ATiiE04bgTcUPgRMsjEbX7+l9LBgaCz3K1qpVofEbqqqx0OvQUkyhQuW8rftD6flfzOaWa4cuCR1SYxFYpGDhk2YnWFgsFgYMGICiKKxbty7U1QktusZi61aYPVu81nfhgkFNplDBXJw2pXCzofKx6NtXHDdtEsfafCxsNs+8DDV9p6kRHy+OhYWe7wdKsIDq5nbBECx0Z/jNm13XGgqNhcUirnf7dvG/LsT6k9aSxwJcSSQPHxZH6V8hCQTeBItQbPy1VGrKY9HU59AA0+wEi2nTppGZmRnqajQN2rVzvc7JgU6d4Jprgld+UzOFCmW42VBrLHTBYvNmcfSlsXAXPKuaQzU3jYX7hAme997fpme+BItAOm9nZkJGhni+VqwQ7wWzT8fGuvrt3r2uvhWI6F+6xqI1CRY5OeLYync3JQFCaiwCi9RY+KRZCRY///wz8+bN47nnngt1VZoGiiLCuQF07gxr1ngKG4GmqZtCtSaNxUknieOePSJxnK+dE7PZtUiu6qPQXHwsfGks9PYPxOKwqmARDB8LcJlD/fyzOAazbRQFevUSr/v2FX05LQ3atvV/WaF03g62KZQePU1qLCSBRAoWgcVbHovmMocGmGYjWBw5coQbbriBDz/8kCjdeVMCr7wCjz0G69a5TBeChb4rrKoue/1QaCz0XWN9F7k1aizS0sROqKoK07iaFkq+HLibu8YiN1cc09P9X2ZVrVgwTKHAla1W93PQo0MFi48/Fn1LZ/DgwPgD6M7bVqun709Ly7wN1U2hWvkiRBIgqo6Tqura+GrqY3xzQGosfNIsepeqqlx99dXcfPPNDBkyhH379tXpexaLBYtbwqUi7QGzWq1YqzoJBgm9XL+V37+/K3pMsK/J4UB/fKwWCxiNGCwWjIBDUbAHqz7h4ZgBtaQEm9WKoaICI2A3GnH4oQ51aTMDiDJtNo8yTTYbCmBTVdQA3w9jnz4YFi3Ctn49ZGZiAlSTCVuVck0xMSgnTmArKPCok1JRgQlwGI3Ba7sGoERHi3oWFHjUU9m/X7yfmen358wUFibasbQU1WrFWFaGAbCZzYFt1/PPxxQZiaIJMtY77gjuc96nD8qrr2LSBBx7jx5+eaaqYTS6xpKSEmfkJP35sYLfr1uJiBD9paQERS9HVQN+f5X4eDHxalovNSys2jPanPD7nCbxD5GR4pmyWMQzZTK5njFNwJBt1gi09Y9qtzufX33tEYg5NNTPWX3KDalgMX36dB599NEaz1m5ciVLly6lqKiIBx54oF6/P3PmTK+/P2/evJBrPebPnx/S8v2BqbycSdrr3z77DEtiIt23bKEPcODwYdbPmROUephLSjgbUCor+fm77+izcyfdgN0HD7LVj3Woqc167dlDT2D/nj1sdCvz9OJiooGlK1aQX3WH3c+cFB9PV+DorFnsnziRkUBhWRl/VLkH41SVOGD5b7+Rd+yY8/32q1czCMjLz+evILVdQ0jZupXRQElODr+71bPz/PlkAUcMBlZobeWv52xcZSVxwIo//+RYUREnZ2eTDKzZupXDAb5XGXfcwYDXX2fD9deTvWmTy0E/WISFMbpvX1I2b+avtDSOB+B6FZuN87TXC3/+GatmpnRuZSUKsODPP6nQfTz8ROrmzYwCDMuWOd9b8McfVAQ40WPs/v2Md/u/pLKSBU34easrLWFOa0kodrvzmfr1m2+wRUZyrvb/74sWQUyMbLNGEHHiBGcAqs3GHO357bxuHVnA4bw8VgXomQ5Vm5WVldX5XEVVq8acDB55eXnk5eXVeE6nTp245JJL+OGHH1DcVPB2ux2j0chll13G+++/7/W73jQW7du3Jy8vjzhdTRhkrFYr8+fPZ8KECZhbgLrMOGoUhlWrsD/1FI6778bw+OMYH3sM+w034Hj99eBUwmbDrAmK1sOHMTz2GMY33sD+73/jmD690T9flzYzzJiBccYM7DfdhOPVV53vm7p3R9m/H9vixajDhjW6LjWyeTPmgQNRFQXHM89gvPdeHEOGYF+61OM04+jRGFauxPbVV6jnnut8X3nvPUw33ojj7LOxf/ttYOvaCJTVqzGNHInavj223bud7xsefhjj009j/8c/sDz3nF+fM+OIERjWrMH23XeoZ53l+v/bb1HPPrvRv9/kKSuDHTtgwIDA/L6qCs2Mw0HZrl2YO3QAVcWs+bZYs7P9bgamLFuG6ZRTPN6zHjwYGFM6d3JzxfVpqCedhG3NmsCWGUBa2pzWkjAlJKCUlWHdtg3atMGshTouy81l/rJlss0aw5EjmNu3B8CqmV8bXn5ZzLuXXIL9gw/8Wlyon7OioiJSUlIoLCysdf0cUo1FSkoKKbq9aQ288sorPP74487/c3JyOOOMM5g9ezbDhw/3+b3w8HDC3ZNbaZjN5pA/TE2hDn7hhhtg1SqM776Lcdo059vGsDCMwbo+s1nYaFdUYLZYnDaPxshIv9ahxjbT3jeqqmeZWl1M4eGBt7scMADOPhtlzhyMWoADQ0wMhqrlakKYqbLSs07aHoPBbK7+naaElgxQKSz0bA/Nx8LYvr3zfb89Z5oPgMluF/dM27Awxca2Dnva+HiXI3mAUCMioKwMs90u2szNkdscFeX/+9ytm7CTdjiE0JKcjDkjI/D+UFUEFyU8vEXMBS1mTmtJxMeLZ6q83CNSnlkbz2SbNQK3iIBmo1HcX83EzBAeHrA5NFRtVp8ym4WPRQe33R2AGE1N3rVrV9oFMwqSpDqXXgp33ili3Ls7DQc76kRsrLBZLi4OjfN2qKNC6UybBnPmuJJvTZ1a/RzdEbmqzWRzc94uLhb3W58ws7PFMRDhqEMRbra1ERkpNCP6vXXvn4F4ljMyxLgVFxdcp/iwMFGmbhopF3aSQBEXJ4IEFBUFNs9Pa8T9Htps4rmWzttAM4oKJWmixMa6djKXLw/d4lQPHVlcHJpws7VFhfJ3XgVfnHKKqz2iouDKK6uf40uwaC7JffRws6rqGdlKzwsQiHCoVQUL/SgFC/+h30v93gZasADo2jX4kbbAFXIWZLhZSeBwjwzlPjc19TG+OeAuWOj3trnMoQGmWQoWnTp1QlVVBgTK3ldSP0aMEMdly0ITbhacUWRavcZCUUT4YYMBbrvNtQh3R78vVXMGNJcY3BERrv7l7hCvaywCIVj4SsIoF4X+QwvdqwRLYxFK3E2AW9q1SZoOvgSLYG10tWTc1zhVBYtW/ky3brFK4h90P5fly+HUU8XrUJhCQdPSWKiqqA+4BvhgcOaZcOKE655UpWpCQZ3mYgqlKEJgOn5cJMlr107kItAT5gXDFEo/evHhkjQQ/V7qyQf159hoDEzujFDiLlhI4VQSKNwFC318Nxha3vMUCmrSWLRywUKKrZLGo2ssNm50Le5CqbEIxcPtLVlORYVr8a5F4wga8fG+d6Wau48FVE/+pJtBRUcHRoirKlhIjYXfUXVTqKqCRXPoj/XF3RSqlS9CJAHEm8ZC+lf4B/dxSdeyNhetf4CRgoWk8WRmQseOwgzou+/Ee6HSWJSUhDbztrspVEGB6zPdB6Qp4Etj0ZwWcrqJly7IuptBBWI3ThcsKiuFJkpqLPyPnsW8qilUS5ykO3d2vZbCqSRQSMEicBiNYt0DIhQ3tOwxqx5IwULiH665Rhzz88WxtTlve9NY6IJFTdqDUNDcfSygusYikBGhwFNjYbM5Q/NKwcKPVNVYNKf+WF8mTnS9lgs9SaCQgkVg6dtXHPXknVKwAKRgIfEXt9ziGSEnlD4WTU1jEWwzqNpo7j4WUF1jEciIUOApWLjfN7nb7D98+Vi0xElaNx8FWLcuZNWQtHD0uefYsdAFVmnJ9Okjjlu2iGNLHrPqgRQsJP4hNRUmTXL93xR8LJqKxqKpChYtyccikBGhwDMqlG4GBVJj4U/0qFCtQbBwf8aOHg1dPSQtm969xXHjxtDlmGrJSI2FV6RgIfEfo0a5XrdWjYU+eEPTFSx8mUI1Rx8L3fQumKZQ+n1TFDlJ+xNfztstdZJesEBk4Z41K9Q1kbRU+vcXx23bYMUK8VqOWf5DFyx0jYW+0RUdHZr6NBGawQpC0mxwV++H0nk7FBoL3Ylr40bXe01VsKjNFKo5LOT0+717tzgG0xTK3XFbhm30G2pUlHhRWiqOLV2wGDcOcnNDXQtJS6ZtWxGB7Phxlx9k166hrVNLQtcIHTki7vGhQ+L/du1CV6cmgNRYSPzHoEGu1wcPBrdsb87bwVyQjBoldvoPHIB9+8R7umCRmBi8etSFlmAKVdW2NdCmUN40FtK/wr/oz7CeTV0XMFr57p9E0mAUBdwTCU+aBD/9FLLqtDhiYqBTJ/F6wwbXukcKFhKJn3B33tbNGYKFN1OoYC78oqNhyBDx+o8/xLG5aiyag2Ch7xRt3y7qrWssAm0KVVkpQ80GCu0ZVvSkkvqxKYVqlkiaG2lprtczZ0JSUujq0hIZOFAcly1z+Uu1bx+6+jQBpGAh8S/ffgunnw7TpgW3XHfBQrdzdBd0goGedXzhQnFsqoJFbeFmm4Ng0amTaF+LBVaudGlfMjICU54vUyiJ/6iqsdCPvjLISySS2rn4YnHs0wf69QttXVoiuqXGjz+KY0SEZwLMVogULCT+ZfJkmD8/+BK7vvjYtEk49IaFQY8ewa3DuHHiOHeuiA7VVAULX6ZQzcmm3WiEXr3Ea12ITUsLnJZKmkIFHNV9c8D9KDUWEknDmTxZLHoXLQp1TVomusZi6VJxbNeu1fveScFC0jKouqs5ZEjwd5RPPVX4U+TmikG8qQsWzVljAS5zqMWLxVG3dQ0E3sLNSo2Ff5EaC4nE/yiK8K1o5bvoAcPdtxRavX8FSMFC0lKouqs5cmTw6xAWBhdcIF5/9lnTFSxagikUeDolXn45vPpq4MqSGovAI30sJBJJcyMjQ4SN1mnl/hUgBQtJS6FNG5GkT8c9p0YwueQScfz4Y1fEoqYmWLSEqFAAN98ML70EO3bAhx/CsGGBK0v6WAQe95DR7kepsZBIJE2ZyZNdr6XGQgoWkhZCWBh8/rl4bTbD6NGhqce4cSKfR2mpa2HUVAULXwnymoOPBYjs23fcAd27B74sqbEIOKoeVlZ/bnSNhRQsJBJJU+b++12vpYZVChaSFsSpp8Lq1fD7756qyWBiMAiTHHfnraYmWLQUU6hgIsPNBp6qztu6gCEnaolE0pTp3BkeekjM9RddFOrahBwpWEhaFoMGhU5boTNkCHz5JcTHV7e/bAq0FFOoYOJNYyEFC/+i+1iUlIDDITUWEomk+TBjBpw4ERwNehNHChYSSSCYOlVkg962remZzLSUqFDBxJuPRVNr1+aOuwBRViadtyUSSfOilYeZ1ZErCIkkUOg2402NluJjEUx0waKiQppCBYrISFSDAUXXVkjnbYlEIml2SI2FRNLakD4W9ScxURwLC8VuOkiNhb9RFGwREeJ1cbHUWEgkEkkzRAoWEklrQ/pY1J+kJJea+/BhcZQaC79ji4wUL6TGQiKRSJolUrCQSFob0sei/phMLq3FoUPiKDUWfscpWJSUSI2FRCKRNEOkYCGRtDZ8mULpGgwpWHhHT8CYnS2OUmPhd5ymUAUFwp8FpMZCIpFImhFSsJBIWhu1mUJJ523vSMEi4Dg1Frq5GUjBQiKRSJoRzUqw+Omnnxg+fDiRkZGkpKQwderUUFdJIml+SFOohlFVsJCmUH7HKVjk5Iij2Szvs0QikTQjms0K4quvvuKGG27gySefZPz48aiqysaNG0NdLYmk+aEv1Ox2kYjMoO0vSMGiZnTBQlXFUWos/E41jYXUVkgkEkmzolmsIGw2G3fccQfPPvss1113nfP9nj17hrBWEkkzxd3UyWp1LZClj0XN6IKFjtxJ9zvVBAvpuC2RSCTNimZhCrVmzRqys7MxGAwMHDiQjIwMzjrrLDZv3hzqqkkkzQ/3BbG7OZT0saiZqoKF1Fj4Hafztm5uJjUWEolE0qxoFluTe/bsAWD69Om88MILdOrUieeff56xY8eyY8cOkpKSvH7PYrFg0bPkAkVFRQBYrVasVR1Xg4RebqjKl9SfFtdmioIuOlhLS0FbzJlsNhTAqqrVHbubGYFoMyUhwWPAtBuNOJr5fWpKWK1WSjMyxD/r1gHgiI7GLu9xk6bFjY+tANlmzY9Qt1l9yg2pYDF9+nQeffTRGs9ZuXIlDocDgAcffJALLrgAgHfffZd27drxxRdfcNNNN3n97syZM73+/rx584iKimpk7RvH/PnzQ1q+pP60pDY7T1FQVJXf5s7FouVnmGSxYAJ+//NPynfsCG0F/YQ/2yx1715Guf2/fts2Ds6Z47ffl0D40KHwxhvO/w/GxrJO3uNmQUsaH1sLss2aH6Fqs7Kysjqfq6iq7okYfPLy8sjLy6vxnE6dOvHXX38xfvx4/vzzT04++WTnZ8OHD+f000/niSee8PpdbxqL9u3bk5eXR1xcnH8uop5YrVbmz5/PhAkTMEuTk2ZBS2wzU2wsisWCddcu6NBBvBcdjWK1Yt2zB9q1C3ENG0dA2mztWszDhzv/tX3wAeoll/jntyXONjv31lsxaKZQ1uXLYeDAENdMUhMtcXxs6cg2a36Eus2KiopISUmhsLCw1vVzSDUWKSkppKSk1Hre4MGDCQ8PZ/v27U7Bwmq1sm/fPjp27Ojze+Hh4YR7sYM2m80hf5iaQh0k9aNFtVlYGFgsmFVV+FS4mT+Zo6JajJ+FX9ssM9PjX1MLuk9NCfUf/4CHHoI+fTAPGxbq6kjqSIsaH1sJss2aH6Fqs/qU2Sx8LOLi4rj55pt55JFHaN++PR07duTZZ58F4KKLLgpx7SSSZkjVJHmauSEgo0L5Ii0NjEYRphek83aAcNx5J8akJJgyJdRVkUgkEkk9aTYriGeffRaTycQVV1xBeXk5w4cPZ8GCBSRq9uESiaQe6LsPelQoPSIUSMHCF2Fh0Lkz7Nrl+l/if8LC4B//CHUtJBKJRNIAms0Kwmw289xzz/Hcc8+FuioSSfOnavZtKVjUjZ49XYKF1FhIJBKJROJBs8hjIZFI/ExVwcI9lJy0ufVNr16u11JjIZFIJBKJB1KwkEhaI7rwoAsUUmNRN3r2dL2WGguJRCKRSDyQgoVE0hrxZQqlKGCQw4JP3AULqbGQSCQSicQDuYKQSFojvgQLqa2oGXfBInQpgCQSiUQiaZJIwUIiaY1UNYXSj9K/ombS0lyvm3kSQYlEIpFI/I3cnpRIWiNSY9EwFAUOH4aSEpChriUSiUQi8UCuIiSS1ogULBpOmzahroFEIpFIJE0SaQolkbRGqmbeloKFRCKRSCSSRiIFC4mkNVI187b0sZBIJBKJRNJIpGAhkbRGIiLEsbxcHKXGQiKRSCQSSSORgoVE0hqJiRHH0lJxlIKFRCKRSCSSRiIFC4mkNaILFiUl4igFC4lEIpFIJI1EChYSSWukqmAhfSwkEolEIpE0EilYSCStEamxkEgkEolE4mekYCGRtEaqCha5ueIYGxua+kgkEolEImn2SMFCImmNVBUsFi0Sx5EjQ1MfiUQikUgkzR4pWEgkrZGqgsUff4jj2LGhqY9EIpFIJJJmjxQsJJLWiLtgkZ0Nu3eDwQCjR4e2XhKJRCKRSJotUrCQSFoj7oLF0qXi9YABEB8fsipJJBKJRCJp3kjBQiJpjbgLFkeOiNdduoSuPhKJRCKRSJo9UrCQSFoj7oKF7mchI0JJJBKJRCJpBFKwkEhaI7pgUVoKRUXitRQsJBKJRCKRNAIpWEgkrRFdsABXDgv39yQSiUQikUjqiRQsJJLWSGQkKIp4ffiwOEqNhUQikUgkkkYgBQuJpDWiKC4NhRQsJBKJRCKR+IFmI1js2LGDyZMnk5KSQlxcHKNHj+b3338PdbUkkuaLFCwkEolEIpH4kWYjWEyaNAmbzcaCBQtYvXo1AwYM4JxzziFXtw+XSCT1Qxcsjh71/F8ikUgkEomkATQLwSIvL49du3Zx//33k5WVRffu3XnqqacoKytj8+bNoa6eRNI8qSpISI2FRCKRSCSSRtAsBIvk5GR69+7NBx98QGlpKTabjf/+97+kp6czePDgUFdPImmeSMFCIpFIJBKJHzGFugJ1QVEU5s+fz+TJk4mNjcVgMJCens7cuXNJSEjw+T2LxYLFYnH+X6TF67darVit1kBX2yt6uaEqX1J/WmqbGWNiPHYWrOHh0EKusaW2WUtGtlnzRLZb80O2WfMj1G1Wn3IVVVXVANalRqZPn86jjz5a4zkrV65k8ODBTJkyBavVyoMPPkhkZCRvv/0233//PStXriQjI6Nev//JJ58QFRXll2uQSJorQ559lrZLljj/n/fWW5SnpYWwRhKJRCKRSJoaZWVl/P3vf6ewsJC4uLgazw2pYJGXl0deXl6N53Tq1IklS5YwceJE8vPzPS6oe/fuXHfdddx///1ev+tNY9G+fXvy8vJqvTGBwmq1Mn/+fCZMmIDZbA5JHST1o6W2mfHGGzG8957zf2tuLiQlha5CfqSltllLRrZZ80S2W/NDtlnzI9RtVlRUREpKSp0Ei5CaQqWkpJCSklLreWVlZQAYDJ4uIQaDAYfD4fN74eHhhIeHV3vfbDaH/GFqCnWQ1I8W12bx8R7/mhMToSVdHy2wzVoBss2aJ7Ldmh+yzZofoWqz+pTZLJy3R44cSWJiIldddRXr169nx44d3Hvvvezdu5dJkyaFunoSSfOkQwfX67Aw8SeRSCQSiUTSQJqFYJGSksLcuXMpKSlh/PjxDBkyhMWLF/Pdd9/Rv3//UFdPImmedOniei0jQkkkEolEImkkzSIqFMCQIUP45ZdfQl0NiaTl4C5YyOR4EolEIpFIGkmz0FhIJJIA0Lmz63XoYjhIJBKJRCJpIUjBQiJprbibP9USnU0ikUgkEomkNqRgIZFIQIu8JpFIJBKJRNJQpGAhkUgkEolEIpFIGo0ULCSS1ky3bqGugUQikUgkkhaCFCwkktbM99/D+PGwcGGoayKRSCQSiaSZ02zCzUokkgDQuzf89luoayGRSCQSiaQFIDUWEolEIpFIJBKJpNFIwUIikUgkEolEIpE0GilYSCQSiUQikUgkkkYjBQuJRCKRSCQSiUTSaKRgIZFIJBKJRCKRSBqNFCwkEolEIpFIJBJJo5GChUQikUgkEolEImk0UrCQSCQSiUQikUgkjUYKFhKJRCKRSCQSiaTRSMFCIpFIJBKJRCKRNBopWEgkEolEIpFIJJJGYwp1BYKJqqoAFBUVhawOVquVsrIyioqKMJvNIauHpO7INmt+yDZrfsg2a57Idmt+yDZrfoS6zfR1s76OrolWJVgUFxcD0L59+xDXRCKRSCQSiUQiaT4UFxcTHx9f4zmKWhfxo4XgcDjIyckhNjYWRVFCUoeioiLat2/PwYMHiYuLC0kdJPVDtlnzQ7ZZ80O2WfNEtlvzQ7ZZ8yPUbaaqKsXFxWRmZmIw1OxF0ao0FgaDgXbt2oW6GgDExcXJB7qZIdus+SHbrPkh26x5Itut+SHbrPkRyjarTVOhI523JRKJRCKRSCQSSaORgoVEIpFIJBKJRCJpNFKwCDLh4eE88sgjhIeHh7oqkjoi26z5Idus+SHbrHki2635Idus+dGc2qxVOW9LJBKJRCKRSCSSwCA1FhKJRCKRSCQSiaTRSMFCIpFIJBKJRCKRNBopWEgkEolEIpFIJJJGIwWLIPKf//yHzp07ExERweDBg/nzzz9DXaVWy6JFizj33HPJzMxEURS+/fZbj89VVWX69OlkZmYSGRnJqaeeyubNmz3OsVgs/POf/yQlJYXo6GjOO+88Dh06FMSraF3MnDmToUOHEhsbS1paGlOmTGH79u0e58h2a1q88cYbZGVlOWOvjxw5kp9//tn5uWyvps/MmTNRFIU777zT+Z5st6bH9OnTURTF469NmzbOz2WbNU2ys7O5/PLLSU5OJioqigEDBrB69Wrn582x3aRgESRmz57NnXfeyYMPPsjatWsZM2YMZ511FgcOHAh11VolpaWl9O/fn9dee83r58888wwvvPACr732GitXrqRNmzZMmDCB4uJi5zl33nkn33zzDZ999hmLFy+mpKSEc845B7vdHqzLaFX88ccf3HrrrSxbtoz58+djs9mYOHEipaWlznNkuzUt2rVrx1NPPcWqVatYtWoV48ePZ/Lkyc6JUbZX02blypW89dZbZGVlebwv261p0rdvXw4fPuz827hxo/Mz2WZNj/z8fEaPHo3ZbObnn39my5YtPP/88yQkJDjPaZbtpkqCwrBhw9Sbb77Z471evXqp999/f4hqJNEB1G+++cb5v8PhUNu0aaM+9dRTzvcqKirU+Ph49c0331RVVVULCgpUs9msfvbZZ85zsrOzVYPBoM6dOzdodW/NHD16VAXUP/74Q1VV2W7NhcTERPXtt9+W7dXEKS4uVrt3767Onz9fHTt2rHrHHXeoqiqfs6bKI488ovbv39/rZ7LNmib33XefevLJJ/v8vLm2m9RYBIHKykpWr17NxIkTPd6fOHEiS5cuDVGtJL7Yu3cvubm5Hu0VHh7O2LFjne21evVqrFarxzmZmZmcdNJJsk2DRGFhIQBJSUmAbLemjt1u57PPPqO0tJSRI0fK9mri3HrrrUyaNInTTz/d433Zbk2XnTt3kpmZSefOnbnkkkvYs2cPINusqfL9998zZMgQLrroItLS0hg4cCCzZs1yft5c200KFkEgLy8Pu91Oenq6x/vp6enk5uaGqFYSX+htUlN75ebmEhYWRmJios9zJIFDVVXuvvtuTj75ZE466f/bubOQqro/jOOPetTyFM2kVmSZWaE0KM0FZUFFdGFBRGDTzTFsoCComy4irYuEjDKKMBtAigwqggZSo6BRpdMADdoElRXNg2b+3oto0/lb8f7bpce37wc2dNZeZ7m3D8d62LmSJJFbsPL7/WrTpo0iIyPl8/l08OBBDRgwgLyCWFFRkcrLy5WTk9PoHLkFp2HDhmnXrl06duyYtm/frsePH2vkyJF6/vw5mQWpqqoq5efnKyEhQceOHZPP59PixYu1a9cuSS33s+Zplq/6lwoJCQl4bWaNxhA8fiUvMm0aWVlZunLlis6cOdPoHLkFl8TERFVWVurly5c6cOCA5syZo7KyMuc8eQWXBw8eaMmSJTp+/LhatWr1w3nkFlwmT57s/Dk5OVkjRoxQfHy8CgsLNXz4cElkFmwaGhqUmpqq7OxsSdLgwYN17do15efnKyMjw5nX0nLjiUUT6Ny5s8LCwhq1x5qamkZNFM3v604aP8srOjpadXV1evHixQ/n4M9YtGiRDh06pJKSEnXv3t0ZJ7fgFBERoT59+ig1NVU5OTkaOHCgNm7cSF5B6vLly6qpqVFKSoo8Ho88Ho/KysqUl5cnj8fjfN/JLbh5vV4lJyfr1q1bfNaCVExMjAYMGBAw1r9/f2dTn5aaG8WiCURERCglJUUnTpwIGD9x4oRGjhzZTFeFH+nVq5eio6MD8qqrq1NZWZmTV0pKisLDwwPmPHr0SFevXiXTP8TMlJWVpeLiYp06dUq9evUKOE9uLYOZqba2lryCVFpamvx+vyorK50jNTVVs2fPVmVlpXr37k1uLUBtba1u3LihmJgYPmtBatSoUY22TL9586Z69uwpqQX/ndb0vy/+dyoqKrLw8HDbsWOHXb9+3ZYuXWper9fu3r3b3Jf2V3rz5o1VVFRYRUWFSbLc3FyrqKiwe/fumZnZunXrrF27dlZcXGx+v99mzZplMTEx9vr1a2cNn89n3bt3t5MnT1p5ebmNHz/eBg4caPX19c11W/9pmZmZ1q5dOystLbVHjx45x/v375055BZcVq5caadPn7bq6mq7cuWKrVq1ykJDQ+348eNmRl4txbe7QpmRWzBavny5lZaWWlVVlZ07d86mTp1qbdu2df6NQWbB58KFC+bxeGzt2rV269Yt27t3r0VFRdmePXucOS0xN4pFE9q8ebP17NnTIiIibMiQIc42mWh6JSUlJqnRMWfOHDP7ss3b6tWrLTo62iIjI23s2LHm9/sD1vjw4YNlZWVZx44drXXr1jZ16lS7f/9+M9zN3+F7eUmygoICZw65BZf58+c7P/O6dOliaWlpTqkwI6+W4n+LBbkFn5kzZ1pMTIyFh4dbbGyspaen27Vr15zzZBacDh8+bElJSRYZGWn9+vWzbdu2BZxvibmFmJk1z7MSAAAAAP8V/I4FAAAAANcoFgAAAABco1gAAAAAcI1iAQAAAMA1igUAAAAA1ygWAAAAAFyjWAAAAABwjWIBAAAAwDWKBQAAAADXKBYAgF8yd+5chYSENDpu377d3JcGAGgGnua+AABAyzVp0iQVFBQEjHXp0iXgdV1dnSIiIprysgAAzYAnFgCAXxYZGano6OiAIy0tTVlZWVq2bJk6d+6siRMnSpJyc3OVnJwsr9erHj16aOHChXr79q2z1s6dO9W+fXsdOXJEiYmJioqK0owZM/Tu3TsVFhYqLi5OHTp00KJFi/T582fnfXV1dVqxYoW6desmr9erYcOGqbS0tKm/FQDw1+OJBQDgtyssLFRmZqbOnj0rM5MkhYaGKi8vT3FxcaqurtbChQu1YsUKbdmyxXnf+/fvlZeXp6KiIr1580bp6elKT09X+/btdfToUVVVVWn69OkaPXq0Zs6cKUmaN2+e7t69q6KiIsXGxurgwYOaNGmS/H6/EhISmuX+AeBvFGJff+IDAPB/mDt3rvbs2aNWrVo5Y5MnT9bTp0/16tUrVVRU/PT9+/fvV2Zmpp49eybpyxOLefPm6fbt24qPj5ck+Xw+7d69W0+ePFGbNm0kffnvV3Fxcdq6davu3LmjhIQEPXz4ULGxsc7aEyZM0NChQ5Wdnf27bxsA8AM8sQAA/LJx48YpPz/fee31ejVr1iylpqY2mltSUqLs7Gxdv35dr1+/Vn19vT5+/Kh3797J6/VKkqKiopxSIUldu3ZVXFycUyq+jtXU1EiSysvLZWbq27dvwNeqra1Vp06dfuu9AgB+jmIBAPhlXq9Xffr0+e74t+7du6cpU6bI5/NpzZo16tixo86cOaMFCxbo06dPzrzw8PCA94WEhHx3rKGhQZLU0NCgsLAwXb58WWFhYQHzvi0jAIA/j2IBAPjjLl26pPr6em3YsEGhoV/2Ddm3b5/rdQcPHqzPnz+rpqZGY8aMcb0eAODXsSsUAOCPi4+PV319vTZt2qSqqirt3r1bW7dudb1u3759NXv2bGVkZKi4uFjV1dW6ePGi1q9fr6NHj/6GKwcA/FsUCwDAHzdo0CDl5uZq/fr1SkpK0t69e5WTk/Nb1i4oKFBGRoaWL1+uxMRETZs2TefPn1ePHj1+y/oAgH+HXaEAAAAAuMYTCwAAAACuUSwAAAAAuEaxAAAAAOAaxQIAAACAaxQLAAAAAK5RLAAAAAC4RrEAAAAA4BrFAgAAAIBrFAsAAAAArlEsAAAAALhGsQAAAADgGsUCAAAAgGv/AJ+MLBBVyF0cAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RGB signal shape: (600, 3)\n",
      "First 5 rows (RGB values per frame):\n",
      " [[175.3624   7.0082  15.7386]\n",
      " [175.1603   6.9922  15.3166]\n",
      " [175.403    7.1933  15.3035]\n",
      " [175.7301   7.4385  15.2813]\n",
      " [176.2027   7.6379  15.4258]]\n",
      "Filtered PPG signal shape: (600,)\n",
      "First 5 rows of filtered PPG signal:\n",
      " [ 2.62940294e-03  5.57383528e-05 -1.99519378e-02 -6.62919665e-02\n",
      " -1.35670520e-01]\n"
     ]
    }
   ],
   "source": [
    "#12 signal processing backbone sample on one video to show how the signal are extracted from the video\n",
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "def read_and_process_video(video_path, target_frames=600):\n",
    "    cap = cv2.VideoCapture(video_path)\n",
    "    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
    "\n",
    "    rgb_array = []\n",
    "\n",
    "    # Read and process up to 600 frames\n",
    "    for i in range(min(total_frames, target_frames)):\n",
    "        ret, frame = cap.read()\n",
    "        if not ret:\n",
    "            break\n",
    "\n",
    "        # Resize for consistency (optional, can skip if full frame is used)\n",
    "        frame = cv2.resize(frame, (100, 100))  # You can adjust size or remove this line\n",
    "\n",
    "        # Convert BGR (OpenCV default) to RGB\n",
    "        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "        # Compute average pixel intensity for R, G, B channels\n",
    "        avg_rgb = np.mean(frame_rgb.reshape(-1, 3), axis=0)  # Shape: (3,)\n",
    "        rgb_array.append(avg_rgb)\n",
    "\n",
    "    cap.release()\n",
    "\n",
    "    # If fewer than 600 frames, pad with zeros\n",
    "    while len(rgb_array) < target_frames:\n",
    "        rgb_array.append(np.zeros(3))\n",
    "\n",
    "    # Convert to numpy array of shape (600, 3)\n",
    "    rgb_array = np.array(rgb_array)\n",
    "    return rgb_array\n",
    "#*************************************************************************************************\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def extract_and_plot_rgb_signals(rgb_array):\n",
    "    # Extract X(j) = (x1(j), ..., x600(j)) for j = 1, 2, 3\n",
    "    red_signal = rgb_array[:, 0]\n",
    "    green_signal = rgb_array[:, 1]\n",
    "    blue_signal = rgb_array[:, 2]\n",
    "\n",
    "    # Plotting the Raw RGB Signal\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(red_signal, color='red', label='Red')\n",
    "    plt.plot(green_signal, color='green', label='Green')\n",
    "    plt.plot(blue_signal, color='blue', label='Blue')\n",
    "    plt.title(\"Raw RGB Signal\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Average Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return red_signal, green_signal, blue_signal\n",
    "#*****************************************************************************************************\n",
    "from scipy.signal import detrend\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def apply_detrending_filter(red_signal, green_signal, blue_signal):\n",
    "    # Apply scipy's detrend function\n",
    "    red_detrended = detrend(red_signal, type='linear')\n",
    "    green_detrended = detrend(green_signal, type='linear')\n",
    "    blue_detrended = detrend(blue_signal, type='linear')\n",
    "\n",
    "    # Plot the filtered signals\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(red_detrended, color='red', label='Red (Detrended)')\n",
    "    plt.plot(green_detrended, color='green', label='Green (Detrended)')\n",
    "    plt.plot(blue_detrended, color='blue', label='Blue (Detrended)')\n",
    "    plt.title(\"Detrended RGB Signal\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return red_detrended, green_detrended, blue_detrended\n",
    "\n",
    "#****************************************************************************************************\n",
    "# Step 4: Normalize the signals by dividing each by its maximum absolute value\n",
    "def normalize_signal(red_signal, green_signal, blue_signal):\n",
    "    red_normalized = red_signal / np.max(np.abs(red_signal))\n",
    "    green_normalized = green_signal / np.max(np.abs(green_signal))\n",
    "    blue_normalized = blue_signal / np.max(np.abs(blue_signal))\n",
    "\n",
    "    # Plot the normalized signals\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(red_normalized, color='red', label='Red (Normalized)')\n",
    "    plt.plot(green_normalized, color='green', label='Green (Normalized)')\n",
    "    plt.plot(blue_normalized, color='blue', label='Blue (Normalized)')\n",
    "    plt.title(\"Normalized RGB Signal\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Normalized Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return red_normalized, green_normalized, blue_normalized\n",
    "#*****************************************************************************************************\n",
    "from scipy.ndimage import convolve1d\n",
    "\n",
    "# Step 5: Apply a 3x3 kernel moving average filter\n",
    "def apply_moving_average_filter(red_signal, green_signal, blue_signal):\n",
    "    # Define the 3x3 kernel for moving average filter\n",
    "    kernel = np.ones(3) / 3  # This is a 3-point moving average kernel\n",
    "\n",
    "    # Apply the kernel to each signal using convolution\n",
    "    red_filtered = convolve1d(red_signal, kernel, mode='nearest')\n",
    "    green_filtered = convolve1d(green_signal, kernel, mode='nearest')\n",
    "    blue_filtered = convolve1d(blue_signal, kernel, mode='nearest')\n",
    "\n",
    "    # Plot the filtered signals\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(red_filtered, color='red', label='Red (Filtered)')\n",
    "    plt.plot(green_filtered, color='green', label='Green (Filtered)')\n",
    "    plt.plot(blue_filtered, color='blue', label='Blue (Filtered)')\n",
    "    plt.title(\"Filtered RGB Signal with 3x3 Moving Average\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Filtered Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return red_filtered, green_filtered, blue_filtered\n",
    "#********************************************************************************************************\n",
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Step 6: Apply PCA to recover independent source signals\n",
    "def apply_pca_to_rgb(red_signal, green_signal, blue_signal, n_components=3):\n",
    "    # Stack the RGB signals together to form a 2D matrix where each row is a frame and each column is a channel\n",
    "    rgb_matrix = np.vstack([red_signal, green_signal, blue_signal]).T  # Shape: (frames, 3)\n",
    "\n",
    "    # Apply PCA to the stacked RGB matrix\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca_components = pca.fit_transform(rgb_matrix)\n",
    "\n",
    "    # Plot the first three principal components (eigenvectors)\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(pca_components[:, 0], color='red', label='First Principal Component')\n",
    "    plt.plot(pca_components[:, 1], color='green', label='Second Principal Component')\n",
    "    plt.plot(pca_components[:, 2], color='blue', label='Third Principal Component')\n",
    "    plt.title(\"PCA Components (Eigenvectors) of RGB Signal\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"PCA Component Value\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return pca_components\n",
    "#********************************************************************************************************\n",
    "from scipy.signal import butter, filtfilt\n",
    "import numpy as np\n",
    "\n",
    "# Step 7: Apply bandpass filter to the PPG signal (first eigenvector)\n",
    "def bandpass_filter(signal, lowcut=0.4, highcut=4.0, fs=30.0, order=4):\n",
    "    # Create the bandpass filter\n",
    "    nyquist = 0.5 * fs  # Nyquist frequency\n",
    "    low = lowcut / nyquist\n",
    "    high = highcut / nyquist\n",
    "\n",
    "    # Butterworth bandpass filter\n",
    "    b, a = butter(order, [low, high], btype='band')\n",
    "    \n",
    "    # Apply the filter to the signal using filtfilt (zero-phase filtering)\n",
    "    filtered_signal = filtfilt(b, a, signal)\n",
    "    \n",
    "    return filtered_signal\n",
    "\n",
    "# Step 8: Apply PCA and select the first eigenvector (PPG signal)\n",
    "def extract_ppg_signal(r_filtered, g_filtered, b_filtered):\n",
    "    # Stack the signals together to apply PCA\n",
    "    rgb_matrix = np.vstack([r_filtered, g_filtered, b_filtered]).T  # Shape: (frames, 3)\n",
    "    \n",
    "    # Apply PCA to the stacked RGB matrix\n",
    "    pca = PCA(n_components=3)\n",
    "    pca_components = pca.fit_transform(rgb_matrix)\n",
    "\n",
    "    # The first eigenvector (principal component) is the PPG signal\n",
    "    ppg_signal = pca_components[:, 0]\n",
    "\n",
    "    # Apply bandpass filter to the PPG signal\n",
    "    filtered_ppg_signal = bandpass_filter(ppg_signal, lowcut=0.4, highcut=4.0, fs=30.0)\n",
    "\n",
    "    # Plot the original and filtered PPG signals\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(ppg_signal, label='Original PPG Signal', color='blue')\n",
    "    plt.plot(filtered_ppg_signal, label='Filtered PPG Signal (0.4-4 Hz)', color='red')\n",
    "    plt.title(\"PPG Signal Before and After Bandpass Filtering\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return filtered_ppg_signal\n",
    "#****************************************************************************************************\n",
    "def extract_ppg_signal(r_filtered, g_filtered, b_filtered):\n",
    "    # Stack the signals together to apply PCA\n",
    "    rgb_matrix = np.vstack([r_filtered, g_filtered, b_filtered]).T  # Shape: (frames, 3)\n",
    "    \n",
    "    # Apply PCA to the stacked RGB matrix\n",
    "    pca = PCA(n_components=3)\n",
    "    pca_components = pca.fit_transform(rgb_matrix)\n",
    "\n",
    "    # The first eigenvector (principal component) is the PPG signal\n",
    "    ppg_signal = pca_components[:, 0]\n",
    "\n",
    "    # Apply bandpass filter to the PPG signal to get the BVP signal\n",
    "    bvp_signal = bandpass_filter(ppg_signal, lowcut=0.4, highcut=4.0, fs=30.0)\n",
    "\n",
    "    # Plot the original and filtered PPG (BVP) signals\n",
    "    plt.figure(figsize=(8, 4))\n",
    "    plt.plot(ppg_signal, label='Original PPG Signal', color='blue')\n",
    "    plt.plot(bvp_signal, label='Filtered BVP Signal (0.4-4 Hz)', color='red')\n",
    "    plt.title(\"PPG Signal Before and After Bandpass Filtering (BVP)\")\n",
    "    plt.xlabel(\"Frame\")\n",
    "    plt.ylabel(\"Intensity\")\n",
    "    plt.grid(True)\n",
    "    plt.legend()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    return bvp_signal\n",
    "\n",
    "# ---- Example usage ----\n",
    "video_path = \"C:/Users/hp/Desktop/DatasetVideoFiles/DatasetVideoFiles/sample1.mp4\"\n",
    "rgb_signal = read_and_process_video(video_path)  # Step 1\n",
    "r, g, b = extract_and_plot_rgb_signals(rgb_signal)  # Step 2\n",
    "r_d, g_d, b_d = apply_detrending_filter(r, g, b)  # Step 3\n",
    "r_norm, g_norm, b_norm = normalize_signal(r_d, g_d, b_d)  # Step 4\n",
    "r_filtered, g_filtered, b_filtered = apply_moving_average_filter(r_norm, g_norm, b_norm)  # Step 5\n",
    "pca_components = apply_pca_to_rgb(r_filtered, g_filtered, b_filtered)  # Step 6\n",
    "filtered_ppg_signal = extract_ppg_signal(r_filtered, g_filtered, b_filtered)# Step 7\n",
    "\n",
    "print(\"RGB signal shape:\", rgb_signal.shape)  # Should print (600, 3)\n",
    "print(\"First 5 rows (RGB values per frame):\\n\", rgb_signal[:5])\n",
    "\n",
    "print(\"Filtered PPG signal shape:\", filtered_ppg_signal.shape)\n",
    "print(\"First 5 rows of filtered PPG signal:\\n\", filtered_ppg_signal[:5])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
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   "source": []
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